Dialectical generative agent architecture

Abstract

An architecture includes multiple agents that communicate through structured dialectical exchanges to transform business requirements into executable applications. The agents are configured to collaborate and communicate through a series of iterative dialogues, where each agent contributes its specialized expertise to the process. The structured exchanges allow the agents to challenge, refine, and validate each other's outputs to automatically generate an application.

Description

DIALECTICAL GENERATIVE AGENT ARCHITECTURECLAIM TO PRIORITY

[0001] This application claims the benefit of the following provisional application, which is hereby incorporated by reference in its entirety: U.S. Serial No. 63 / 561,608, filed March 5, 2024 (RGLO- 0001-P02).SUMMARY

[0002] All documents mentioned herein are hereby incorporated in their entirety by reference. References to items in the singular should be understood to include items in the plural, and vice versa, unless explicitly stated otherwise or clear from the text. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations of conjoined clauses, sentences, words, and the like, unless otherwise stated or clear from the context.BRIEF DESCRIPTION OF THE FIGURES

[0003] The disclosure and the following detailed description of certain embodiments thereof may be understood by reference to the following figures:

[0004] Fig. 1 depicts aspects of a system utilizing a dialectic agent system.

[0005] Fig. 2 depicts aspects of an example of an architecture of a dialectical generative agent system.

[0006] Fig. 3 depicts aspects of an example of an architecture of a dialectical generative agent system.

[0007] Fig. 4 depicts aspects of an example of a polypartite agent system.

[0008] Fig. 5 depicts aspects of an example of a tripartite dialectical multi-agent architecture for automated application generation.

[0009] Fig. 6 depicts aspects of an example of a method for post-deployment application evolution.

[0010] Fig. 7 depicts aspects of an example of a method for translating application specifications into executable specifications.

[0011] Fig. 8 depicts aspects of an example of a method for automated generation of an application using dialectical generative agents.

[0012] Fig. 9 depicts aspects of an example of a method for agent-integrated application deployment.DETAILED DESCRIPTION

[0013] Existing approaches to business process automation suffer from significant limitations that impede organizations from efficiently translating their operational workflows into executable digital applications. In one example, current methods exhibit a translation gap in that there is a disconnect between business-centric representations of workflows (found in spreadsheets, documents, and verbal descriptions) and the structured data models required by execution platforms. This translationtypically requires specialized technical expertise and significant manual effort. In another example, current methods exhibit context fragmentation in that single-agent artificial intelligence (Al) systems fail to maintain sufficient context when handling complex, multi-stage workflow generation tasks. The single models lack the collaborative intelligence needed to integrate diverse input modalities while producing standardized outputs. In another example, current workflow implementations remain largely static after deployment, unable to adapt to changing usage patterns, emerging inefficiencies, or evolving business requirements without significant manual redevelopment. In another example, current workflow generation systems operate in isolation from execution environments, creating a disconnect where runtime insights and performance data cannot easily influence and improve the original workflow design. In yet another example, current systems fail to identify opportunities for embedded automation within the workflows they generate, requiring separate tools and processes to implement agent-based task automation after deployment.

[0014] Manual solutions are often inadequate when it comes to addressing problems in workflow management. These methods typically involve the generation of a generic workflow blueprint, which tends to be overly broad and fails to capture the specific nuances required for individual business processes. As a result, these blueprints still necessitate the involvement of IT professionals to implement custom workflows or to develop IT use-case specific workflow blueprints that can be utilized effectively. Additionally, any learning or improvements must be manually incorporated by technical users into subsequent processes.

[0015] Large Language Models (LLMs) face significant learning constraints that hinder their effectiveness in workflow generation contexts. While powerful for general text processing, LLMs struggle with the multi-faceted, domain- specific reasoning required to transform business processes into executable workflows. Standard LLMs operate with a context- limited, single-pass architecture that cannot maintain the complex state management necessary to iteratively develop and refine structured workflows. They lack the ability to perform the dialectical reasoning needed to consider business logic, technical constraints, and domain- specific requirements when generating workflows. Furthermore, traditional LLMs have no inherent mechanism to incorporate feedback from actual workflow execution, resulting in static generations disconnected from operational realities. The fine- tuning approach commonly used to enhance LLM performance provides only limited improvement for workflow generation, as it cannot address the fundamental architectural limitations necessary for iterative refinement and multi-modal integration across diverse business domains. These learning issues create a significant barrier to applying standard LLM approaches to the complex problem of automated workflow generation.

[0016] These challenges lead to prolonged implementation cycles, inflexible workflows that rapidly become obsolete, and lost opportunities for continuous enhancement and automation. Organizations require a more advanced approach that encompasses the lifecycle of workflow applications — from initial creation to ongoing development and enhanced autonomy.

[0017] The dialectical generative agent architecture (DGAA) and associated systems and methods described herein utilize a tripartite dialectical generative agent architecture that overcomes the problems outlined above. This innovative approach includes new methods and system for translating requirements into executable workflows while continuously improving through multiple learning mechanisms.

[0018] The DGAA operates through an interplay of specialized agents that communicate via a structured dialectical process. Unlike traditional single-agent systems that process inputs in isolation, this architecture enables multiple agents to engage in a collaborative reasoning process, challenging and refining each other's outputs through continuous dialogue.

[0019] The DGAA includes a tripartite learning framework that enables the system to improve across different time horizons and knowledge domains.

[0020] The DGAA deploys multiple types of agents. In one embodiment, the agents may include architect agents, review agents, coding agents, and administrative agents, each configured and / or for specific functions within the workflow generation process. These agents operate within a dialectical communication structure that enables dialogue through structured conversation prompts, a shared context repository, and an iterative refinement protocol. Operationally, the DGAA transforms requirements (e.g., business requirements, process specification, etc.) into executable workflows through a multi-stage process beginning with input reception and analysis, where the administrative agent processes initial specifications. Architect agents then create preliminary workflow models identifying components such as stages, tasks, approval flows, and data requirements. This is followed by dialectical refinement, where review agents evaluate the models and provide feedback, which architect agents use to improve the designs iteratively until quality thresholds are met. The system then creates an intermediate representation of the workflow in an abstraction layer that captures functional requirements independent of implementation details before coding agents transform this abstraction into executable specifications compatible with target execution platforms. Once deployed, the generated workflow's performance, usage patterns, and effectiveness can be monitored, enabling the system to generate recommendations for improvements or automatically implement changes.

[0021] Fig. 1 provides a schematic representation showing a system 100 for the use of DGAA. The system is designed to facilitate the automated generation of applications by leveraging aplurality of dialectical generative agents. These agents engage in a structured dialectical process, allowing them to collaboratively reason and refine outputs through dialogue. The system can receive input specifications and can transform them into executable applications. The DGAA 104 interacts with various other components, including an input specification 102 for the desired application, and may generate an output application 106.

[0022] The application specification 102 serves as the initial input to the Dialectical Generative Agent System 104. The specification 102 may include descriptions of the desired application, including functional requirements, business logic, and any constraints that need to be adhered to during the generation process. The specification 102 may be provided in a natural language format, which the system interprets and processes to create a structured representation of the application.

[0023] The DGAA 104 may generate an application 106. In embodiments, the application 106 output may be a final executable form of the application, ready for deployment within the target environment. In some embodiments, the Application 106 output may be a machine-readable definition of an application that is designed to be executed on a different platform (e.g., executed on cloud-based services, enterprise software environments, or specialized industry' platforms). The application 106 output is constructed based on the specifications provided and refined through the dialectical process facilitated by the system. The supporting data 108 input provides additional context and information that aids the DGAA 104 in generating the application. This data may include historical performance metrics, industry-specific practices, and domain knowledge that inform the decision-making process of the generative agents. The supporting data 108 is available to the system to allow agents to draw on a rich repository of information to optimize their outputs.

[0024] Fig. 2 provides a schematic representation of an example set of agents that may be incorporated into a dialectical generative agent architecture. This example set includes a variety of agent types, such as an Admin / User Proxy agent, Asset Importer agent, Process Architect agent, Process RAG agent, Process Critic agent, Form Architect agent, Form RAG agent, Form Critic agent, Table to JSON agent, JSON Critic agent, and others. In certain embodiments, some or all of these agents may be based on LLMs, enabling them to perform complex tasks and facilitate the workflow generation process effectively.

[0025] The Admin / User Proxy may be configured as an interface between the user and the system. This component may be configured to receive user inputs, which may be provided through a static prompt with a user input template. The Admin / User Proxy may be configured to verify that the user's requirements are accurately captured and translated into a format that the system can process. This interface interacts with foundational LLMs (e.g., LLaMA, GPT, etc.), which are utilized to interpret and process the natural language inputs from users.

[0026] The Asset Importer may be configured to handle the integration of various business assets into the system. The Asset Importer may operate within a code execution container, which provides a controlled environment for executing the necessary code to transform and import assets. The Asset Importer interacts with foundational LLMs to parse and understand the structure and content of the assets such that they are correctly formatted and ready for further processing.

[0027] The Process Architect may be an agent configured to create preliminary workflow models based on the input received from the Admin / User Proxy. The Process Architect utilizes a static prompt with a variable template to generate workflow models, which include components such as stages, tasks, approval flows, and data requirements. The Process Architect may include fine-tuned LLMs to ensure that the generated models are accurate and align with the user’s specifications.

[0028] The Process Retrieval-Augmented Generation (RAG) agent may be configured to provide specific data related to the workflow from external knowledge stores and platform data. The agent may use variable prompts to query data sources (internal and external data sources), retrieving relevant information that can inform and improve the workflow models created by the Process Architect. The Process RAG agent includes fine-tuned LLMs that enable the effective integration of external data into the workflow generation process.

[0029] The Process Critic may be configured for evaluating the workflow models generated by the Process Architect. This component may use variable prompts to assess the models, providing feedback that includes a natural language description of any identified issues or areas for improvement. The Process Critic interacts with external knowledge stores and platform data to ensure that evaluations are informed by current industry standards and widely accepted practices.

[0030] The Form Architect may be configured for designing the forms that will be used within the generated workflows. This component may operate using variable prompts to create form descriptions that align with the workflow models. The Form Architect 200 leverages fine-tuned LLMs and external knowledge stores to ensure that the forms are both functional. The Form Architect may be configured to translate the abstract workflow models into tangible user interfaces that can be implemented within the application.

[0031] The Form RAG agent may be configured to assist the Form Architect by retrieving specific data related to form design from external knowledge stores and platform data. The agent may use variable prompts to access information, which is subsequently used to enhance the form descriptions generated by the Form Architect. The Form RAG agent may include fine-tuned LLMs.

[0032] The Form Critic may be configured to evaluate the form descriptions created by the Form Architect, providing feedback that includes a natural language description of any identified issues or areas for improvement. The Form Critic may use variable prompts to conduct assessments. TheForm Critic may facilitate iterative refinement through dialectical communication with the Form Architect.

[0033] The Table To JSON component may be configured to convert tabular data into a machine readable format (e.g., JSON format), which serves as a representation that can be utilized by the system’s execution platforms. This component operates using variable prompts to guide the conversion process such that the resulting JSON accurately reflects the structure and content of the original data. The Table To JSON component interacts with fine-tuned LLMs and platform data to maintain an accurate conversion process.

[0034] The JSON Critic may be configured to evaluate the JSON output generated by the Table To JSON component, providing feedback that includes a natural language description of any identified issues or areas for improvement. The JSON Critic may use variable prompts to conduct assessments, drawing on platform data to ensure that evaluations are informed by current industry standards and recommended practices.

[0035] With reference to Fig. 3, in example embodiments, a dialectic agent system 302 for automated generation of an application 402 (see Fig. 4) may include a plurality of dialectic generative agents 304 configured to receive an input prompt and generate an output for configuring the application 400. The agents of this plurality of dialectic generative agents 304 may be configured to communicate with each other dialectically via conversation prompts. In some examples, each (or at least some) of the dialectic generative agents of the plurality of dialectic generative agents may include a separate LLM. And in some examples, one or more of the dialectic generative agents of the plurality of dialectic generative agents 304 may execute iteratively and include at least three levels of learning, as described by example herein.

[0036] The plurality of dialectic generative agents 304 may include at least one of an architect agent 306, a review agent 310 (examples of which may also be described and / or referred to herein as a critic agent), or an administrative agent (also referred to herein as an “admin” agent) 314. The architect agent 306 may be configured to create an object from a prompt including a description of an object to be generated, and to iteratively revise the object based on feedback about the object from the review agent 310.

[0037] The review agent 310 may be configured to receive the object and to generate the feedback about the object. This feedback may include a natural language description of the feedback.

[0038] The administrative agent 314 may be configured to receive the input prompt. This input prompt may be generated with a user input template and may include a natural language description of the application. For example, a user (such as a human user) may enter their input via the user input template, and that input may include, in natural language, a description of what they want theapplication to do. As an example, the application 402 may be for a workflow and the natural language description of the application 402 to be generated may include a description of the workflow. In some examples, the application 402 may be at least one of a static application having a fixed architecture and a fixed set of functionalities, or a dynamic application configured to be dynamically updated based on at least one of a user interaction, a data input, or an external event.

[0039] In some examples, the administrative agent 314 may be configured to identify and initialize the dialectical generative agents of the plurality of dialectical generative agents 304. In an example, one or more of the dialectical generative agents of the plurality of dialectical generative agents 304 may be initialized with a prompt instructing the one or more of the dialectical generative agents of its purpose and configuration.

[0040] The administrative agent 314 may generate the prompt including the description of the object to be generated and provide the prompt including the description of the object to be generated to the architect agent 306. The administrative agent 314 may also provide, monitor, and / or otherwise administrate dialectical communication between the dialectical generative agents, as may be described by example herein. In some examples, at least two of the dialectical generative agents in the plurality of dialectic generative agents 304 communicate using conversation prompts. In an example, the administrative agent 314 may be configured to select which dialectical generative agent of the plurality of dialectical generative agents 304 to analyze an output of another dialectical generative agent of the plurality of dialectical generative agents 304.

[0041] In some examples, the administrative agent 314 may be configured to permit a human user to override or monitor decisions of the administrative agent.

[0042] The dialectic communication provided between the dialectic generative agents by the administrative agent 314 may include iteratively providing the object created by the architect agent 306 to the review agent 310. Also, the dialectical communication provided between the dialectic generative agents by the administrative agent 314 may include iteratively providing the feedback generated by the review agent 310 about the object to the architect agent 306. This administrative agent 314 may monitor and / or manage this iterative process between the review agent 310 and the architect agent 306 until, for example, it observes a stopping criteria. For example, the administrative agent 314 may iteratively provide the object created by the architect agent 306 to the review agent 310, and the feedback generated by the review agent 310 about the object to the architect agent 306, until the administrative agent observes a stopping criteria. In examples, stopping criteria may include a threshold number of iterations, the length of the feedback from the review agent 310, and / or other criteria.

[0043] The administrative agent 314 or another agent of the dialectical generative agents 304 may initialize generation of at least a portion of the output for configuring the application 402 based on the object. For example, the administrative agent 314 may initialize generation of the at least the portion of the output for configuring the application upon observing the stopping criteria.

[0044] In some embodiments, the plurality of dialectical generative agents 304 may include a machine formatting agent 404 to generate the at least the portion of the output for configuring the application 402, and the at least the portion of the output may include a machine-readable description. In an example, the machine-readable description is formatted in at least one of JavaScript object notation (JSON), extensible markup language (XML), or Python and the like.

[0045] In some embodiments, the dialectical agent system 302 may include an abstraction layer to provide a machine-readable description for the application based on the object. In some examples, the abstraction layer may be included in, take the place of, or come functionally before or after a machine formatting agent 404 (see Fig. 4). In some examples, each set of agents in a tripartite (or polypartite) agent system as described by example with reference to Fig. 4 may include such an abstraction layer and / or machine formatting agent 404.

[0046] With reference again to Fig. 3, un some embodiments, the plurality of dialectical generative agents 304 may include a retrieval-augmented generation (RAG) agent 312 configured to provide specific data related to the object based on at least one of an external knowledge store or platform data. The external knowledge store and / or platform data may include data related to the object (e.g., related to a process, customer of the process, data of the process, etc.).

[0047] In an example embodiment, the dialectical agent system 302 may include several sets or “parts” (such as three parts) of a polypartite (e.g., tripartite) architecture, each part including a respective plurality of dialectical generative agents 304 as described by example herein. And in an example, the object of a first part of the at least three parts may be a blueprint for the application, the object of a second part of the at least three parts may be elements for implementing the architecture, and the object of a third part of the at least three parts may be data (such as sample data) for the architecture or the elements.

[0048] For example, in some embodiments, the application 402 may be for a business workflow and the input prompt generated with the user input template may include a natural language description of the business workflow to be implemented by the application 402. The plurality of dialectical generative agents 304 (e.g., for each of the first, second, and third parts) may further include a retrieval-augmented generation (RAG) agent.

[0049] The first part may include a first plurality of dialectical generative agents 408 to generate the blueprint of the application 402. This may include the architect agent including a first architect agent306 configured to create the blueprint of the application 402, and to iteratively revise the blueprint based on feedback about the blueprint from a first review agent 310. This first plurality may also include the RAG agent including a first architect RAG agent 308 configured to query data related to the business workflow, where the first architect RAG agent 308 is configured to identify the data related to the business workflow in at least one of an external knowledge store or platform data. The first architect agent 306 may utilize the data related to the business workflow to create the blueprint of the application 402.

[0050] The first plurality may also include the review agent including the first review agent 310 configured to receive, as an input prompt, the blueprint of the application 402 and to generate the feedback about the blueprint of the application 402. This feedback may include a natural language description. Meanwhile, the first review RAG agent may be configured to query data related to the blueprint. The first review RAG agent 312 may be configured to identify the data related to the blueprint in at least one of the external knowledge store or the platform data. The first review agent 310 may be configured to utilize the data related to the blueprint to generate the feedback about the blueprint of the application. The administrative agent may be configured to iteratively provide the blueprint to the first review agent and the feedback generated by the first review agent about the blueprint to the first architect agent.

[0051] The second part may include a second plurality of dialectical generative agents 410 to generate a form description for the application. This may include the architect agent including a second architect agent 306 configured to be prompted with the blueprint, to create the form description for the application using the blueprint, and to iteratively revise the form description based on feedback about the form description from a second review agent. This second plurality of dialectical generative agents also includes the RAG agent comprising a second architect RAG agent 308, wherein the second architect agent utilizes data queried by the second architect RAG agent to create the form description.

[0052] The second plurality of dialectical generative agents 410 may also include the review agent comprising the second review agent 310 configured to receive, as an input prompt, the form description for the application and to generate the feedback about the form description for the application. This feedback includes a natural language description. Meanwhile, the RAG agent further includes a second review RAG agent 312, where the second review agent utilizes data queried by the second review RAG agent to generate the feedback about the form description for the application. The administrative agent includes a second administrative agent 314 configured to iteratively provide the form description to the second review agent and the feedback generated by the second review agent about the form description to the second architect agent.

[0053] The third part may include a third plurality of dialectical generative agents 412 to generate a data description of sample data for the form description. This includes the architect agent comprising a third architect agent 306 configured to be prompted with the form description, to create the data description of sample data for the form description using the form description, and to iteratively revise the data description based on feedback about the data description from a third review agent 310. This third part also includes the RAG agent comprising a third architect RAG agent 308, wherein the third architect agent utilizes data queried by the third architect RAG agent to create the data description.

[0054] The third part also includes the review agent comprising the third review agent 310 configured to receive, as an input prompt, the data description of sample data and to generate the feedback about the data description of sample data. This feedback includes a natural language description. Meanwhile, the RAG agent further includes a third review RAG agent 312, where the third review agent utilizes data queried by the third review RAG agent to generate the feedback about the data description of the sample data. The administrative agent includes a third administrative agent 314 configured to iteratively provide the data description to the third review agent and the feedback generated by the third review agent about the data description to the third architect agent.

[0055] As used herein, an LLM is a type of artificial intelligence system trained on text data to understand and generate human language. These models use neural network architectures, typically based on transformer technology, that enable them to develop contextual understanding of input text recognizing semantic relationships, inferring implicit meaning, and processing information across lengthy contexts. LLMs analyze the relationships between words and phrases in their input, building a nuanced representation that captures the intended meaning rather than just surface-level patterns. The contextual understanding allows the models to generate responses that reflect comprehension of subtleties, ambiguities, and complex ideas present in the prompts they receive.

[0056] As used herein, a fine-tuned LLM is a large language model that has undergone additional training on specialized data to enhance its contextual understanding within specific domains, tasks, or styles. This process starts with a pre-trained base model and then adapts its parameters using smaller, targeted datasets through supervised learning techniques. Fine-tuning deepens the model's contextual grasp of domain-specific terminology, conventions, and reasoning patterns. Fine tuning may be performed for models that are used in the generation or analysis of technical artifacts like application blueprints, form layouts, data schemas, and workflow specifications.

[0057] LLM models may receive an input prompt and generate an output. As used herein, a prompt is to be understood broadly. A prompt is a structured input provided to an Al system to guide itsgeneration of content or execution of tasks. It may include instructions, context, examples, or questions that direct the Al toward producing a specific type of output. Prompts serve as an interface for communicating intentions to Al systems. A prompt may have multiple modalities and may include text prompts, context documents, few-shot examples (e.g., sample input-output pairs that models interpret contextually to infer patterns of reasoning or response formats appropriate to the task), structured data (e.g., information in formats like JSON or CSV that models contextualize within their understanding of data structures and relationships), multimodal inputs, code snippets, and the like.

[0058] As used herein, a variable prompt is a dynamic input structure that contains both fixed elements and changeable parameters that can be adjusted based on context, user needs, or system state. Variable prompts incorporate elements that can be programmatically modified, populated with different values, or adapted to specific situations.

[0059] As used herein, a RAG agent refers to a specialized Al system that combines information retrieval capabilities with generative language models. These agents operate by first retrieving relevant information from external knowledge sources such as databases, documentation, websites, or knowledge bases in response to a query or task. The retrieved information is then processed and incorporated into the context provided to a generative model, which uses this augmented knowledge to formulate a response that integrates both its pre-trained knowledge and the specifically retrieved information. RAG agents can access domain-specific knowledge, technical specifications, industry standards, and historical implementation data to inform the generation process.

[0060] FIG. 5 shows a schematic representation of a tripartite dialectical generative agent architecture, comprising three distinct parts 502, 504, and 506. Each part includes a set of specialized agents and components designed to collaboratively generate and refine outputs for configuring applications. In embodiments, each part 502, 504, 506 may include the plurality of dialectic generative agents 304 described with respect to Fig. 3. The components within these parts include various agents such as Admin, Architect RAG, Critic RAG, Machine Formatting Agent, Validators, Element RAG, Element Generator, Data RAG, and Data Generator, each interacting dialectically to achieve the system's objectives.

[0061] In part 502, the Admin agent coordinates the activities of the Architect RAG agent and Critic RAG agent. The Architect RAG agent may be configured for creating initial designs or blueprints, while the Critic RAG agent evaluates these designs, providing feedback for iterative refinement. The Machine Formatting agent processes the refined designs into machine-readable formats, and Validators ensure the output meets predefined standards and requirements. As described herein, the agents may iterate through multiple cycles of design and evaluation to determine a suitable blueprintfor the application that meets the initial specification. This iterative process involves communication and feedback loops between the agents. Initially, the Architect RAG agent generates a preliminary design or blueprint based on the input specifications and any relevant data retrieved from external knowledge sources. This initial blueprint is then subjected to scrutiny by the Critic RAG agent, which evaluates the design against predefined criteria and industry standards. The determined blueprint may then be translated into machine readable format (e.g., JSON format) through iterations between the critic and validators. The blueprint, which may be represented by machine readable format and / or text description, may be provided to the second part 504.

[0062] The second part 504 may receive the blueprint representation from the first part and use iterative communications between the agents described herein to generate element specifications. In embodiments, the agents in this part 504 may be focused on interface design, where specialized form agents are employed to create comprehensive user interaction elements. The element agents may generate any type of interfaces ranging from data collection interfaces to complete graphical user interfaces (GUIs), interactive dashboards, navigation flows, and end-to-end user journey mappings. The form agent utilizes retrieved specifications and design patterns to construct interfaces that balance aesthetic considerations with functional requirements, creating visually coherent systems that guide users intuitively through complex processes. The agents can design the visual elements and the behavioral aspects of interfaces, including responsive adaptations for different devices, state transitions between screens, input validation mechanisms, error handling displays, and accessibility accommodations. The second part 504 may include an Admin agent that oversees the Generator RAG and Review RAG agents. The Element RAG agent may be configured to retrieve and integrate relevant data, while the Element Generator creates detailed elements based on the retrieved data. The Critic RAG agent assesses these elements, offering feedback to ensure they align with the overall application architecture. The Machine Formatting component and Validators work together to finalize the elements into executable formats. The representation of the design may be provided to the third part 506.

[0063] Part 506 may receive the representation of the design identify specific data for the application. The Critic RAG agent reviews these datasets, providing feedback to ensure they are accurate and comprehensive. The Machine Format component converts the datasets into machine- readable formats, and Validators verify their integrity and compliance with application requirements.

[0064] In embodiments, the output of the dialectical generative agent architecture system is a specification for the application, detailing the application’s architecture, functionalities, and operational parameters. This specification may initially be generated in an intermediate format,which is designed to be flexible and adaptable, allowing for subsequent translation into a format specific to the target application environment.

[0065] In one example, a sample conversation between a set of agents is described below. The example conversation starts with an Admin agent communicating with a Process Architect agent. The Process Architect agent further communicates with a Critic agent, and the Critic agent further communicates with a Process Architect agent.

[0066] In the example of the conversation, an Admin agent may initiate a conversation. The conversation may start with the Admin generating a prompt for the Process Architect agent to initiate the definition of a blueprint. The prompt generated by the Admin agent may be generated from templates based on user input to the system. The exchange between the agents may be as follows:

[0067] Administrative Agent to Architect Agent: "Generate a blueprint for an Engineering Change Order (ECO) workflow for an automotive manufacturing company. The workflow should handle the complete lifecycle of design changes to vehicle components, including initiation, technical review, impact assessment, approval, implementation, and verification. Consider industry standards for automotive manufacturing and ensure compliance with ISO 9001 and IATF 16949 requirements. The workflow should accommodate both urgent safety-related changes and standard improvement modifications."

[0068] Architect Agent to Administrative Agent: "I've created a blueprint for the Engineering Change Order workflow:WORKFLOW: Automotive Engineering Change Order Process DESCRIPTION: Systematic process for managing, evaluating, approving, and implementing engineering changes to vehicle components in automotive manufacturing.STAGE 1 : ECO InitiationTask 1 : Identify Change Requirement (safety concern, cost reduction, quality improvement) Task 2: Draft Initial ECO Document (part numbers, current design details, proposed changes) Task 3: Classify ECO Priority (emergency, high, medium, standard) Task 4: Assign ECO Identification NumberSTAGE 2: Technical ReviewTask 1 : Conduct Engineering Feasibility AnalysisTask 2: Perform Design Verification PlanningTask 3: Create Technical Documentation UpdatesTask 4: Generate Prototype Requirements (if applicable) STAGE 3: Cross-Functional Impact AssessmentTask 1: Evaluate Manufacturing Impact (tooling, fixtures, assembly processes)Task 2: Determine Supply Chain Impact (supplier capabilities, lead times) Task 3: Assess Quality Control ImplicationsTask 4: Analyze Cost Impact (BOM updates, tooling investments) Task 5: Review Regulatory Compliance Considerations STAGE 4: Approval ProcessTask 1 : Department-Level Review (Engineering, Manufacturing, Purchasing, Quality) Task 2: Financial Approval (based on cost threshold matrix) Task 3: Executive Approval (for high-impact changes)Task 4: Customer Notification / Approval (when required by contracts) [additional workflow blueprint stages]."

[0069] Administrative Agent to Critic Agent: "Please review the Engineering Change Order workflow blueprint below for an automotive manufacturing company. Evaluate it for compliance with industry standards, completeness, efficiency, and risk management. Identify any missing elements, potential bottlenecks, or opportunities for improvement: [Administrative Agent includes the blueprint from the Architect Agent] "

[0070] Critic Agent to Administrative Agent: "The blueprint provides a comprehensive foundation for an automotive ECO process but requires several enhancements to fully meet industry standards and operational needs: PPAP Documentation Missing: The workflow doesn't explicitly address Production Part Approval Process (PPAP) requirements. Change Effectivity Tracking: The blueprint lacks a mechanism for tracking effectivity dates and vehicle identification numbers (VINs). Risk Assessment Framework: While impact assessment is included, a formal FMEA (Failure Mode and Effects Analysis) is not specifically mentioned, which is a standard automotive industry requirement for design changes. Temporary Deviation Process: The workflow should include provisions for temporary deviations that may be needed during the transition period while implementing changes.”

[0071] Administrative Agent to Architect Agent: "Please revise the Engineering Change Order workflow blueprint for automotive manufacturing based on the following feedback: [Administrative Agent includes the critique from the Critic Agent] Pay particular attention to incorporating PPAP documentation requirements, change effectivity tracking, FMEA risk assessment, temporary deviation handling, and more comprehensive testing protocols."

[0072] Architect Agent to Administrative Agent: "I've revised the blueprint based on the feedback:

[0073] WORKFLOW: Automotive Engineering Change Order Process (Revised) DESCRIPTION: Comprehensive process for managing, evaluating, approving, and implementing engineering changes to vehicle components in automotive manufacturing, with full compliance to IATF 16949 and ISO9001 standards. [Blueprint included with all revisions implemented, including PPAP updates, comprehensive testing protocols, and other identified improvements...]"

[0074] This dialogue may continue iteratively between the agents until the workflow meets the requirements or when a stopping criteria is met. Once the blueprint is finalized in the first stage, the blueprint is subsequently passed to the second stage, where the focus shifts to generating a detailed form description based on the blueprint. The second stage involves the use of Form Architect agents, which are tasked with creating comprehensive form descriptions that align with the blueprint's specifications. The form descriptions are iteratively refined through a dialectical process involving feedback from Critic agents, which assess the forms for completeness and adherence to industry standards. Upon completion of the second stage, the refined form descriptions are then advanced to the third stage, where Data Generator agents are employed to create sample data sets. These data sets are designed to populate the forms and simulate real-world usage scenarios, thereby enabling thorough testing and validation of the forms' functionality. The third stage ensures that the forms can capture and process the necessary data, thereby completing the transition from blueprint to executable application components.

[0075] In another example, a sample conversation between a set of agents that further include a RAG agent is described below. The example conversation starts with an Admin agent communicating with a RAG agent. The Admin agent then communicates with the Process Architect agent. The Process Architect agent further communicates with a Critic agent, and the Critic agent further communicates with a Process Architect agent.

[0076] In the example of the conversation, an Admin agent may initiate a conversation. The conversation may start with the Admin generating a prompt for the RAG agent. The prompt generated by the Admin agent may be generated from templates based on user input to the system. The exchange between the agents may be as follows:

[0077] Administrative Agent to RAG Agent: "Retrieve current automotive industry standards for Engineering Change Order processes, with specific focus on IATF 16949 requirements, PPAP documentation needs, and change management best practices from leading OEMs. Also retrieve any recent regulatory updates from the past year that might affect ECO processes."

[0078] RAG Agent to Administrative Agent: "I've retrieved the following relevant information: IATF 16949:2016 Section 8.5.6 requires documented processes for change management including customer notification requirements for product or process changes. PPAP Fourth Edition mandates full resubmission of production part approval documents for significant engineering changes, specifically requiring: Design records of changes, Engineering change documentation. OEM’srecent ECO framework emphasizes digital twin synchronization for all engineering changes with effectivity tracking at component level.”

[0079] Administrative Agent to Architect Agent: "Generate a blueprint for an Engineering Change Order (ECO) workflow for an automotive manufacturing company. The workflow should handle the complete lifecycle of design changes to vehicle components, including initiation, technical review, impact assessment, approval, implementation, and verification. Consider the following industry requirements retrieved by the RAG agent: [Administrative Agent includes the information provided by the RAG Agent] Ensure the workflow accommodates both urgent safety-related changes and standard improvement modifications while maintaining compliance with the mentioned standards and best practices."

[0080] Architect Agent to Administrative Agent: "Eve created a blueprint for the Engineering Change Order workflow aligned with current industry standards:WORKFLOW: Automotive Engineering Change Order Process DESCRIPTION: Systematic process for managing, evaluating, approving, and implementing engineering changes to vehicle components in automotive manufacturing, compliant with IATF 16949:2016 and incorporating industry best practices.STAGE 1 : ECO InitiationTask 1 : Identify Change Requirement (safety concern, cost reduction, quality improvement) Task 2: Draft Initial ECO Document using standardized templateTask 3: Classify ECO Priority and Safety Impact (ASIL rating if applicable per ISO 26262) Task 4: Perform Toyota A3-based Root Cause Analysis for problem-driven changes Task 5: Define Change Effectivity Points (VIN ranges, production dates, serial numbers) Task 6: Determine if Temporary Deviation Request is required during implementation [Blueprint continues with details aligned with retrieved information...]"

[0081] Administrative Agent to Critic Agent: "Please review the Engineering Change Order workflow blueprint below for an automotive manufacturing company. Evaluate it against the industry requirements provided by our RAG agent and identify any gaps or improvement opportunities: [Administrative Agent includes the blueprint from the Architect Agent and the information from the RAG Agent]"

[0082] Critic Agent to Administrative Agent: "The blueprint addresses many of the requirements but has several gaps when compared against the retrieved industry standards: Cybersecurity Assessment Missing: The workflow doesn't incorporate requirement for cybersecurity impact assessments for electronic component changes. Digital Twin Integration Insufficient: Requirementfor digital twin synchronization is mentioned but lacks detailed implementation tasks. [Additional critique points...]"

[0083] Administrative Agent to RAG Agent: "Retrieve specific details on GM's Global Change Management System (GCMS) cybersecurity assessment requirements and Volkswagen's digital twin synchronization process requirements to help address gaps in our ECO workflow blueprint."

[0084] RAG Agent to Administrative Agent: "Here are the specific details requested: Cybersecurity Assessment Requirements (January 2024): [data documents follow]"

[0085] Administrative Agent to Architect Agent: "Please revise the Engineering Change Order workflow blueprint to address the gaps identified by the Critic, incorporating these specific details provided by our RAG agent: [Administrative Agent includes the critique from the Critic Agent and the additional information from the RAG Agent]."

[0086] This dialogue may continue with the Architect revising the blueprint, the Critic providing additional feedback, and the RAG Agent retrieving further information as needed, until a comprehensive blueprint is developed or stopping criteria is met. Once the blueprint is finalized in the first stage, the blueprint is subsequently passed to the second stage, where the focus shifts to generating a detailed form description based on the blueprint. The second stage may include form, critic, and admin agents described herein and may also include the use of one or more RAG agents. A Form RAG agent may query data sources to enhance form design and functionality within an application generation system. The data sources may include form templates containing established field structures and naming conventions, as well as comprehensive field type libraries with standard validation patterns and input constraints for various data types. The agent may access industryspecific form requirements to ensure regulatory compliance, such as Health Insurance Portability and Accountability Act (HIPAA)-compliant fields for healthcare or specific disclaimers for financial applications. In one example, the agent may retrieve accessibility standards like Web Content Accessibility Guidelines (WCAG) to ensure forms accommodate users with disabilities.Localization information including language-specific elements, date formats, and address structures may be queried to support international applications. In another example, the data may include security patterns and best practices for protecting sensitive form data would be incorporated, along with domain-specific terminology appropriate to the business context.

[0087] Upon completion of the second stage, the refined form descriptions are then advanced to the third stage, where Data Generator agents are employed to create sample data sets. These data sets are designed to populate the forms and simulate real-world usage scenarios, thereby enabling thorough testing and validation of the forms' functionality. The third stage may include data, critic, and admin agents described herein and may also include the use of one or more RAG agents.

[0088] A Data RAG agent can query a wide spectrum of information related to data structuring, sample generation, data management practices, and the like. The data may include industry-specific data formats and standards for proper representation of information for particular domains, such as healthcare coding systems or financial reporting structures. The agent may access repositories of realistic but anonymized sample data patterns that reflect typical user inputs for various fields, helping to generate representative test data that mirrors real- world usage. Historical data distributions and relationships would be examined to ensure that the generated sample data maintains appropriate correlations between related fields, preserving data integrity. The agent could query regulatory compliance requirements regarding data types that have specific formatting or validation needs, like personally identifiable information or financial records. By synthesizing information from these diverse sources, the Data RAG agent helps create sample data that effectively tests application functionality.

[0089] In some implementations, the parts of the process may be executed in parallel. For example, the agents and part that generates the blueprint description, form description, and data descriptions may be executed without the use of the outputs of other parts of the process. In embodiments, each of the descriptions may be generated independently. After the descriptions are generated, another process may be used to resolve the blueprint, form, and data descriptions into one process.Resolving the blueprint, form, and data descriptions may include determining compatible descriptions (i.e., determining that the blueprint matches the generated forms), identifying incompatible data (i.e., determining if generated data does not have a corresponding form), filtering unmatched descriptions, and the like.

[0090] In embodiments, the dialectic generative agent architecture may be configured to generate a static application. The static application may be generated with a fixed architecture and a fixed set of functionalities that remain constant unless manually updated by the developer or user.

[0091] In embodiments, the dialectic generative agent architecture may be configured to generate an application that can be dynamically updated after deployment. In embodiments, the architecture and / or the set of functionalities may be updated based on user interactions, data inputs, external events, and the like.

[0092] In embodiments, the dialectical generative agent architecture may include one or more monitoring agents that monitor aspects such as application usage, data entry, time of entries, user modifications to the application, usage of data fields, and the like. The monitoring agents may identify patterns in the entries (for example, data seldom being entered in some fields), changes in workflows (for example, breaking up a workflow into smaller steps), data dependencies (for example, workflow steps always being performed together), and the like.

[0093] In embodiments, the monitoring agents may generate a report or a summary of the changes and / or usage interactions. In some implementations, the report may be a natural language report that describes the changes and / or interactions using sentences. In some implementations, the report may be another representation, such as a diff report between user changes and the original application specifications or any other format. The report and / or summary may be provided to one or more agents of the dialectical generative agent architecture. The report may be used by other agents to intellect new prompts or modifications to prompts between agents to determine modifications to the application based on the data from the monitoring agents.

[0094] Dynamic modification of an application may result in changes such as deletion of steps of a workflow, addition of data fields, changes in the specification of required data, verification requirements, timing constraints on steps of the workflow, and the like. Each change in the application may affect existing data in the application, data dependencies, and other workflow parameters. In embodiments, dialectic generative agent architecture may be configured to evaluate and identify data and workflow migration changes that do not affect the data integrity and goals of the workflow.

[0095] In embodiments, an application may be dynamically modified through a feedback mechanism that begins with the current application specification and integrates a specialized agent dedicated to providing targeted feedback. This feedback agent may be configured as an interpreter that translates real- world application usage data into actionable intelligence. The agent may analyze monitoring data, including user interaction patterns, performance metrics, and feature utilization statistics, and transforms these observations into structured feedback prompts that can be injected into the dialectical exchanges between generative agents.

[0096] In one embodiment, the dialectic generative agent architecture may maintain a historical record of its operations, preserving aspects related to the application generation process. The historical archive may include the sequence of prompts that guided agent behaviors, the record of data exchanges that occurred between specialized agents, and / or the contextual information that informed decision-making throughout the generation process. The system can selectively modify specific prompts that guided particular application components, adjust the nature of inter-agent exchanges that produced certain features, or enhance the contextual understanding that informed design decisions. By applying data-driven modifications to the saved generation history, the system can regenerate the application with targeted improvements, avoiding the computational cost and potential disruption of complete regeneration.

[0097] In one example, a system for post-deployment application evolution may be configured to adapt and enhance application functionality based on actual user interactions and data inputs withoutnecessitating manual redevelopment. The system comprises a monitoring infrastructure, a plurality of dialectical generative agents, and an administrative agent. In one example, the monitoring infrastructure may be integrated within a deployed application and may be configured to track user interactions and data inputs. In another example, the monitoring infrastructure may be part of an external application that externally monitors application activity such as data access, errors, interactions, workflow completion metrics, and the like.

[0098] A system for post-deployment application evolution may include a plurality of dialectical generative agents to analyze the monitored data and implement application modifications. These agents may include one or more of a pattern recognition agent, a suggestion agent, an impact analysis agent, and / or an implementation agent. The pattern recognition agent may be configured to analyze the tracked user interactions and data inputs, identifying usage patterns, inefficiencies, and enhancement opportunities by processing the collected data. The suggestion agent may be configured to generate modification proposals based on the identified patterns, utilizing natural language processing to create comprehensible proposals. These proposals include natural language descriptions of recommended changes to the application. The impact analysis agent evaluates the potential effects of the modification proposals on existing application functionality, assessing risks, dependencies, and potential disruptions to ensure safe and effective application evolution. The implementation agent may be configured to translate the approved modification proposals into executable code changes or changes to the intermediate representation. The agents described in the system for post-deployment application evolution, including the pattern recognition agent, suggestion agent, impact analysis agent, and implementation agent, may each operate as separate, specialized entities, each focusing on their distinct roles within the process. Alternatively, these functions may be integrated into a single, multifunctional agent capable of performing all tasks, thereby streamlining the workflow, and reducing the complexity of agent interactions.

[0099] The system for post-deployment application evolution may further include an administrative agent that coordinates dialectic communication between the generative agents. The administrative agent may manage the flow of data and instructions among the agents by providing collected monitoring data to the pattern recognition agent, facilitating iterative refinement of modification proposals through dialogue between the pattern recognition agent and the suggestion agent, directing the impact analysis agent to assess the feasibility of proposals, and instructing the implementation agent to execute approved modifications. In embodiments, that system may implement application modifications while maintaining application availability using techniques such as hot swapping, where components of the application are updated in real-time without requiring a full system restart. Additionally, the system may include a modular architecture, where individual modules ormicroservices can be independently updated and deployed, ensuring that the rest of the application continues to function normally.

[0100] In another example, a system for automated post-deployment updates of applications may include a monitoring agent, a pattern recognition agent, a suggestion agent, an administrative agent, and a dialectical agent system. The monitoring agent may be configured to track user interactions and data inputs within the application. A pattern recognition agent may be included in the system to analyze the tracked user interactions and data inputs. This agent identifies patterns and trends by evaluating the collected data, ultimately generating an analysis result. The system may further include a suggestion agent that receives the analysis result from the pattern recognition agent. The suggestion agent may be configured to generate modification suggestions based on the identified patterns and trends, providing a natural language description of the proposed changes. These descriptions may be designed to be easily understood by both automated systems and human operators

[0101] The example system may include an administrative agent configured for overseeing the monitoring process and managing communication between the system's components. The monitoring process is initiated with a predefined configuration, including a natural language description of the application's functionalities. The administrative agent may facilitate communication by providing the analysis result from the pattern recognition agent to the suggestion agent and returning the generated modification suggestions for further analysis. The example system may further include a dialectical agent system that implements at least a portion of the modification suggestions into the application. The dialectical agent system may receive the natural language descriptions provided by the suggestion agent to apply updates to the application.

[0102] The example system for post-deployment application evolution may include features that enhance its adaptability and effectiveness in real-world environments. One of these features involves the monitoring infrastructure incorporating configurable tracking parameters. These parameters enable the system to dynamically adjust the detail and scope of tracked user interactions based on observed application usage patterns. By tailoring the monitoring intensity, the system ensures that it captures relevant data while minimizing unnecessary overhead.

[0103] In embodiments, the suggestion agent of the example system may be configured to prioritize modification proposals by evaluating the predicted impact on user efficiency, computational resource utilization, and alignment with business objectives. This prioritization process allows the system to focus on modifications that provide value to both users and application stakeholders. To ensure proposed modifications are safe and effective, the impact analysis agent utilizes a simulation environment. This environment tests the modifications against historical usagedata before actual implementation, providing a predictive assessment of potential impacts and reducing the risk of introducing disruptive changes.

[0104] In embodiments, the implementation agent may include additional control through the use of feature flagging. This mechanism allows the system to selectively expose modifications to specific subsets of users before full deployment. By testing changes in a controlled manner, the system can gather real- world feedback and refine updates before a broader rollout. The administrative agent may further evaluate marketplace offerings, recommending specific integration points within the application to optimize performance and feature set.

[0105] In embodiments, the system may maintain an abstraction layer between the functional representation of the application and its executable implementation. This design facilitates modifications across different execution platforms, promoting versatility and reducing development complexity when adapting to new environments or technologies. The dialectical generative agents within the system may operate on an internal document representation of the application. This representation can be exported, manually modified, and reimported, allowing human-guided changes to be integrated alongside automated modifications. This dual approach ensures that the system remains adaptable to both automated and manual input, enhancing its flexibility and usefulness in dynamic application development scenarios.

[0106] With reference to Fig. 6, an example method for post-deployment application evolution may include tracking 610 user interactions and data inputs, analyzing 615 the collected data through a plurality of dialectical generative agents, coordinating communication between agents, and implementing modifications. The method may include tracking, via a monitoring infrastructure, user interactions and data inputs within an application. The collected data may be analyzed by a plurality of dialectical generative agents to identify opportunities for application modifications. The analysis process may include one or more stages. First, a pattern recognition agent may be used to analyze 620 the tracked user interactions and data inputs to identify usage patterns, inefficiencies, and potential enhancement opportunities. A suggestion agent may then be used to generate 625 modification proposals based on the identified patterns. The proposals may include natural language descriptions of recommended changes. The natural language output makes the suggestion both machine-readable and accessible for human review.

[0107] The method may further include the use of an impact analysis agent to evaluate 630 the potential effects of the modification proposals on existing application functionality. This evaluation process includes assessing how proposed changes might influence performance, compatibility, and the overall user experience. Approved modification proposals are translated 635 into executablecode changes by an implementation agent. This translation process involves converting high-level modification proposals into concrete, deploy able updates.

[0108] An administrative agent may be used to manage 640 the flow of information by providing 645 collected monitoring data to the pattern recognition agent, facilitating 650 iterative refinement of modification proposals through dialogue between the pattern recognition and suggestion agents, directing 655 the impact analysis agent to assess proposal feasibility, and instructing 660 the implementation agent to execute approved modifications. The method may further include implementing 665 the application modifications.

[0109] The example system for post-deployment application evolution may incorporate advanced monitoring, analysis, and suggestion capabilities to enhance its adaptability and efficiency. The monitoring agent may be configured to track a wide range of user interactions, including mouse clicks, keyboard inputs, and touch gestures. By capturing these granular data points, the system gains a comprehensive understanding of how users interact with the application, enabling deeper insights into user behavior and application performance.

[0110] The pattern recognition agent may be configured to determine unused features of the application through an analysis of the frequency and duration of user interactions with specific functionalities. In embodiments, the suggestion agent may be designed to generate modification suggestions that include changes to data input types and the order of operations within the application. In addition to general usage patterns, the pattern recognition agent is capable of recognizing specific patterns related to user behavior, such as frequent errors or common navigation paths. The suggestion agent may incorporate user feedback to refine and personalize modification suggestions. Furthermore, the monitoring agent may be configured to categorize data inputs based on their source and type.

[0111] The dialectic generative agent architecture may include a multi-level learning framework that enables progressive improvement through various learning mechanisms. The learning process allows the framework to learn, over time, how to improve the application generation. At the first level, the system learns through immediate collaboration among its dialectical agents during application generation. This initial learning layer captures the insights that emerge from the iterative dialogue between specialized agents, as they propose, critique, and refine solutions collectively. The architectural agents, review agents, and administrative agents engage in structured discourse that produces increasingly refined outputs, with each cycle of feedback and revision enhancing the quality and coherence of the generated application. This collaborative refinement process creates an immediate learning loop where the system improves its output during the generation of a single application. The first level of learning may include the use of RAG agents to identify data related tothe customer of the application. The RAG agents may identify data from external data sources, internal databases, previous applications, and the like and update the External Knowledge Stores and Platform Data. In the first level of learning, the models may be configured to match general configurations and elements (i.e., naming conventions, process flows, fields, data requirements, authorization structure, etc.) that were used in previous similar applications by a customer.

[0112] The second level of learning extends beyond individual generation sessions to identify patterns across multiple applications and execution environments. At this level, the system analyzes data from applications in production, tracking how generated workflows perform in real- world conditions and identifying common patterns of success or failure. This aggregated operational data enables the system to develop domain-specific optimizations, refining its understanding of which architectural patterns, interface designs, and automation strategies prove most effective in particular industries or use cases. By leveraging historical performance data across multiple deployments, the system develops increasingly improved templates and generation strategies tailored to specific business contexts. The use of the applications for different customers may be used to fine-tune LLMs to enable the LLMs to better predict specific customer preferences and needs in applications.

[0113] The third level of learning incorporates explicit human feedback and expert knowledge, creating a mechanism for the system to align with organizational preferences, specialized domain knowledge, and evolving business requirements. This human-in-the-loop learning layer captures expert insights that might not be evident from operational data alone, including strategic priorities, aesthetic preferences, and domain-specific constraints. As human users interact with generated applications and provide feedback on their effectiveness, the system systematically captures these insights and incorporates them into its generation process. This continuous integration of human expertise helps the system overcome limitations in its understanding, address edge cases, and align its outputs with human expectations and organizational culture. Through the tripartite learning framework, the dialectical system achieves continuous improvement across multiple time horizons and knowledge domains.

[0114] In one example, a system for the automated generation of an application may include a multi-level learning structure. The system may include dialectic generative agents that is configured to receive an input prompt and generate an output for configuring the application. The agents may communicate with each other dialectically through conversation prompts, enabling iterative refinement of generated objects based on feedback as described herein.

[0115] The example system may include features to manage three levels of learning processes to optimize application generation. The first-level learning process involves collaboration among the dialectical generative agents to produce an initial workflow output from the input prompt. Duringthis process, an administrative agent manages communication and iterative refinement through feedback mechanisms. The second-level learning process focuses on customizing the initial workflow output based on industry-specific requirements and historical data. This includes maintaining a knowledge base of terminology, workflows, and best practices, as well as identifying common structures, components, and relationships that recur in similar applications. The third-level learning process integrates human feedback into the workflow generation process. The system records human modifications to generated applications, analyzes patterns in human edits, and identifies systematic preferences and corrections. It distinguishes between domain- specific preferences and universal improvements, incorporating these insights into the first- and second-level learning processes to enhance the system's ability to generate high-quality applications autonomously.

[0116] An example method for the automated generation of an application using dialectical generative agents that includes a multi-level learning process may include the methods described herein is depicted in Fig. 8. The method enables dynamic and iterative creation of applications by leveraging natural language processing, agent-based collaboration, and multi-level learning processes. The method may include the processes of generating an application using the dialectical generative agent architecture described herein (see operations 810-860 in Fig. 8). The method also incorporates three levels of learning processes to enhance the efficiency and accuracy of application generation. The first-level learning process involves collaboration among the dialectical generative agents to produce an initial workflow output from the input prompt 865. During this phase, the administrative agent manages communication and iterative refinement through structured feedback mechanisms. A second-level learning process customizes the initial workflow output by incorporating industry-specific requirements and historical data 870. This process involves maintaining a knowledge base of relevant terminology, workflows, and best practices while identifying common structures, components, and relationships in applications. The third-level learning process integrates human feedback into the workflow generation process 875. The system records human modifications to generated applications, analyzes patterns in these edits, and identifies systematic preferences and corrections. It distinguishes between domain- specific preferences and universal improvements, incorporating these insights into the first- and second-level learning processes to refine future outputs.

[0117] In embodiments, intelligent agents may be incorporated into an active or deployed application. Embedded agents can perform tasks such as processing data and executing routine tasks without requiring manual intervention, creating more responsive and autonomous software systems.

[0118] When generating applications with built-in agent capabilities, the DGAA may include analysis to identify which functions of an application are suitable candidates for automation. This analysis considers factors such as task repetitiveness, structured input-output patterns, decision complexity, and potential value from automation. The evaluation process examines workflow components, user interactions, and data processing requirements to determine which elements would benefit most from agent-based automation. This identification process can be performed through both static analysis of the application specification and dynamic analysis that considers how users interact with similar applications over time, creating a prioritized list of automation opportunities.

[0119] Based on this task analysis, the DGAA may identify and implement an agent integration framework for the agents. This framework may include standardized communication protocols between the application and its embedded agents, context sharing mechanisms, execution monitoring capabilities, and safety constraints to prevent undesirable agent behaviors. The framework may enable modular agent deployment to allow new agent capabilities to be added after deployment without significant modifications to the core application code.

[0120] In embodiments, the framework may include a monitoring capability that can allow applications to evaluate their own operation at runtime and identify new automation opportunities. Through monitoring of user interactions, data flows, and task completion patterns, the application can detect repetitive workflows or inefficient processes that could benefit from agent automation. The self-analysis capability enables applications to progressively increase their autonomy over time, proposing or implementing new agent-based automations based on observed usage patterns and operational data, creating an application that becomes increasingly efficient with use.

[0121] Automated agent deployment may support various levels of automation, from rule-based agents that handle structured tasks to sophisticated agents capable of complex decision-making and learning from interactions. In some implementations, the framework may also include a "shadow mode" capability where agents can run in parallel with manual processes without affecting production operations, allowing for validation before full deployment. Additionally, the framework may include monitoring tools that track agent performance, identify potential issues, and provide transparency into automated operations.

[0122] In one example, a system for agent-integrated application deployment may be configured to generate and deploy applications with built-in task automation capabilities. The system may include a plurality of dialectical generative agents that collaborate through structured communication to automate tasks within the application dynamically. The system may include an analysis agent configured to evaluate an application specification and identify tasks suitable for automation. The analysis agent may iteratively refine the identified tasks based on feedback such that tasks with highautomation potential are selected. The framework architect agent then designs an agent integration framework tailored to the identified tasks. This framework enables the runtime deployment of automation agents within the application, allowing automated processes to be executed.

[0123] The system may include a code generation agent configured to generate the application code that implements the functionality of the application and incorporates the agent integration framework. The administrative agent may be configured to oversee this process by receiving an input specification that outlines the application requirements. The agent may coordinate dialectical communication between the generative agents, managing the flow of information from the analysis agent through to the code generation agent. The administrative agent may facilitate iterative exchanges between the analysis agent and the framework architect agent. This iterative process refines the automation strategy, maximizing efficiency and effectiveness. Upon completion, the administrative agent directs the code generation agent to embed the agent integration framework into the final application code and subsequently deploys the application.

[0124] The system may include an agent integration framework that is configured to enable the runtime instantiation of automation agents, allowing new agents to be introduced without redeploying the application. The framework establishes communication channels between the application and the automation agents, enabling real-time data exchange and coordination. Additionally, the framework configures the automation agents to execute the identified tasks, facilitating continuous application enhancement and operational efficiency.

[0125] Further enhancing the system's capabilities, the framework architect agent can generate a graph-based architecture that defines interaction pathways and dependency rules for automation agents within the application. The system may also include a monitoring agent that tracks the performance of automation agents, collects runtime execution metrics, and provides feedback to refine future task automation strategies. This monitoring capability supports a feedback loop that enhances the system's learning processes and improves automation efficiency over time.

[0126] The code generation agent is capable of dynamically creating adapter interfaces, which enable the integration of external automation agents into the application framework. This flexibility allows the application to incorporate third-party automation tools, extending its functionality and adaptability to evolving business needs.

[0127] The agent integration framework incorporates advanced features such as a resource allocation mechanism that dynamically assigns computational resources to automation agents. It also includes fallback strategies to handle agent execution failures, ensuring that the application remains stable and operational even when automated tasks encounter issues.

[0128] The analysis agent may include an evaluation process to identify tasks suitable for automation, focusing on criteria such as repetitiveness, structured input-output patterns, and computational intensity. These criteria help prioritize tasks that offer the greatest benefit from automation, contributing to overall application efficiency.

[0129] A modular agent framework implemented by the framework architect agent allows for the dynamic addition of new automation capabilities without requiring modifications to the core application code. This modularity supports agile development practices, enabling the system to adapt quickly to new automation opportunities.

[0130] The administrative agent further enhances automation efficiency by implementing a progressive automation schedule. This schedule increases the scope of automated tasks incrementally based on successful performance metrics and user feedback. By gradually expanding automation, the system may minimize or reduce risks associated with large-scale changes while continuously improving application performance.

[0131] With reference to Fig. 9, an example method for agent-integrated application deployment may be used to create applications with built-in automation capabilities. The method begins when an administrative agent receives 910 an input specification that outlines the application's requirements. An analysis agent then conducts an evaluation 915 of this specification, systematically identifying 920 tasks and processes within the proposed application that are suitable candidates for automation.

[0132] The method may include identifying automation opportunities and includes the generation 925 of a specialized agent integration framework tailored to the application's specific needs. The framework provides the infrastructure that facilitates automation agents to be deployed and operate within the application during runtime.

[0133] The method may include coordinating 930 dialectical communication between the various generative agents involved. Coordinating may include feeding 935 the application specification to an analysis agent, facilitating 940 iterative exchanges between the analysis and framework architect agents to optimize the integration framework, and directing 945 a code generation agent to incorporate this framework into the actual application code.

[0134] The code generation agent can then produce 950 the application code that implements the core functionality described in the application specification but also integrates the agent framework designed by the architect. When deployed 955, the application includes the integrated framework that enables 960 runtime instantiation of automation agents, allowing them to be created and initialized as needed rather than being permanently coded into the application structure.

[0135] The deployed framework establishes 965 communication channels that facilitate interaction between the application and its automation agents, enabling data exchange, command transmission,and status reporting. Additionally, the framework configures 970 these agents to execute the specific tasks identified earlier in the process, directing their capabilities toward the automation opportunities that will create the most value.

[0136] In some embodiments, the DGAA may generate an intermediate specification that is a technology-agnostic representation of the application before translating it into an executable application. The intermediate specification may capture functional requirements, data structures, business rules, graphical user interfaces descriptions, workflow patterns, and the like in a standardized format that is independent of any specific implementation technology. By creating this abstraction layer between the conceptual application design and its technical implementation, the system enables greater flexibility in how the final application is realized. The intermediate specification can be translated into different technology stacks, deployment environments, or platform- specific formats as needed, allowing the same application design to be efficiently implemented across diverse technical ecosystems without requiring modifications to the core business logic.

[0137] In embodiments, abstraction layers may be used to decouple the logical representation of application functionality from its physical implementation. The abstractions serve as the intermediate representations that capture the characteristics of application components without binding them to specific technologies or platforms. By maintaining application logic at this abstract level, the system can generate different implementation variants optimized for various execution environments without altering the application logic. This approach creates significant flexibility, allowing the same application specification to be realized across diverse technology stacks, cloud environments, or organizational infrastructure configurations. The abstraction layer also simplifies future migrations by isolating the impacts of underlying technology changes from the business functionality, thereby extending application lifespan and reducing maintenance complexity.

[0138] Identified functions of the intermediate specification may be mapped to specific application environments or platforms. The mapping process may consider factors including the computational characteristics of each function, the specialized capabilities of different application platforms, the data processing requirements involved, and the like. The system may suggest specific application environments for particular functions based on their requirements for processing power, data access patterns, or specialized features. For instance, data-intensive analytical functions might be mapped to environments optimized for parallel processing, while user interaction components might be directed to platforms with strong front-end capabilities.

[0139] The division of functions and their strategic mapping across different application environments allows for optimization of efficiency, security, and resource utilization. By analyzingthe interdependencies between components, data flow patterns, and security requirements, the system can determine how to distribute functionality to maximize performance while minimizing vulnerabilities. Computationally intensive operations may be allocated to high-performance computing environments, while sensitive data processing might be restricted to highly secure platforms with appropriate compliance certifications. This division also facilitates scalability by allowing specific functions to be scaled independently as demand fluctuates.

[0140] An intermediate format may employ a structured representation that balances human readability with machine processability, often using a combination of declarative and procedural elements. In one example, JSON or YAML-based schemas may be used for capturing hierarchical relationships and configuration aspects, while UML or similar modeling notations may be used to represent workflows, data structures, and component interactions. In embodiments, any suitable file format or syntax may be used.

[0141] The translation process from intermediate specifications to executable implementations may be performed using one or more agents working in coordinated sequences. In one example, LLM based translation agents may be used for different phases of the conversion process.

[0142] Mapping agents may be configured to analyze the application specification to identify functional components and their relationships, breaking down requirements into discrete units that can be individually processed. Abstraction agents may then transform these identified components into platform-independent representations within the intermediate specification. Distribution agents evaluate the components against various implementation environments, determining allocations based on performance requirements, security considerations, and resource availability. Finally, code generation agents translate the abstracted representations into executable specifications compatible with the target platforms, handling the technical complexities of specific implementation technologies.

[0143] In one example, a system for translating application specifications into executable specifications using a multi-agent translation architecture may convert high-level application requirements into deployable code that is optimized for specific application environments. The system may include a processing unit and a memory storage device that, when executing stored instructions, orchestrate the translation process through a collaborative agent-based approach. The processor may execute instructions that implement a conversion process. The conversion may begin with the system receiving an application specification in a first format. This specification outlines the desired functionalities, performance criteria, and operational requirements of the application. Upon receiving the specification, the system utilizes a multi-agent translation architecture to transform the high-level requirements into executable specifications.

[0144] The translation architecture of the system may include a mapping agent configured to analyze the application specification to identify functional components. The analysis may include breaking down the application into discrete elements such as modules, services, or features, which form the building blocks of the final application. An abstraction agent may be configured to generate abstracted representations of the identified functional components. The abstracted representations are intermediary models that standardize the components, allowing for consistent processing and reducing complexity during the translation process.

[0145] A distribution agent can be configured to evaluate the abstracted components to determine how they should be allocated across multiple application environments. The allocation may be guided by criteria such as efficiency metrics, security requirements, and computational resource availability. The next stage of the process involves a code generation agent, which translates the abstracted representations into executable specifications.

[0146] In another example, a system for translating application specifications into executable specifications using a multi-agent translation architecture may include a multi-agent translation architecture and an output interface that transforms application specifications into executable outputs. The multi-agent translation architecture may be configured to receive an application specification in a first format and generate executable specifications. The application specification may include various input formats such as natural language descriptions, workflow diagrams, flowcharts, or business process models.

[0147] The system may include a mapping agent configured to analyze the application specification and identify functional components. The mapping agent may be configured to break down the application into discrete elements like modules, services, or features that define the application's functionality. The mapping agent may be a machine learning model trained to identify the functional components. In some embodiments, the agent may include pattern recognition algorithms that analyze the application requirements to identify recurrent structures that correspond to common architectural components such as modules, services, or features. In some embodiments, semantic clustering techniques may be used to group related functionality by evaluating linguistic similarities and functional relationships in the specification. In some embodiments, the mapping agent may also include hierarchical decomposition techniques that progressively break down complex functionality into simpler sub-components until reaching an appropriate granularity level. Machine learning-based classification models trained on successful application architectures can recognize patterns in new specifications and suggest optimal component divisions based on historical examples.

[0148] The example system may further include an abstraction agent that may be configured to generate abstracted representations of the identified functional components. These abstracted representations may be an intermediate format that is independent of specific implementation platforms. In some embodiments, the abstraction agent may include capability modeling, which characterizes components by their fundamental operations rather than implementation details. Capability modeling involves analyzing functional descriptions to extract core capabilities and representing them as abstract operations with well-defined semantics. The abstraction agent may further include abstraction algorithms that transform concrete data structures into logical data models that capture relationships and constraints independent of storage technologies. In some embodiments, the abstraction agent may include template pattern matching, where the agent identifies common functional patterns in the application specification and maps them to predefined abstract templates in the intermediate representation. The templates may capture proven solutions to recurring design problems while maintaining technology independence.

[0149] The example system may further include a distribution agent configured to determine how the functional components should be allocated across multiple application environments. The agent may evaluate allocation according to criteria such as efficiency metrics, security requirements, computational resource availability, and the like. The example system may further include a requirements validation agent that verifies the completeness and consistency of the application specification before processing by the mapping agent. In some embodiments, the validation agent may include a natural language model configured to evaluate the clarity and precision of requirement statements, identifying vague language, ambiguous directives, or requirements that lack measurable success criteria. Statistical anomaly detection comparing the current specification against patterns from previously validated applications can be included to highlight unusual omissions or structural differences that may indicate specification deficiencies. The example system may further include a code generation agent that is configured to translate the abstracted representations into executable specifications compatible with the determined application environments.

[0150] The example system may further include an output interface that is configured to provide the executable specifications for implementation. The interface may be configured for packaging and delivering the generated executables. The output interface may implement multiple delivery mechanisms to accommodate various deployment scenarios, including direct API integrations with continuous integration / continuous deployment (CI / CD) pipelines, version-controlled repository submissions, and packaged deployment bundles. In some embodiments, the interface may include documentation generation that accompanies the executable specifications, automatically producing reference materials that explain the implementation details, configuration options, and integrationrequirements. In embodiments, the output interface may generate deployment manifests and configuration templates that facilitate consistent implementation across different environments. In some embodiments, the output interface may be configured for handling of sensitive implementation details and may include encryption for secure transmission and implementing access controls that restrict specification access to authorized personnel. The interface may include validation services that verify the executable specifications against deployment environment requirements before delivery.

[0151] The system may include a knowledge base of previously translated specifications that may be referenced and queried to improve translation accuracy over time by learning from past projects.

[0152] In one example, a method for translating application specifications into executable specifications may employ a multi-agent translation architecture. With reference to Fig. 7, an example method may begin with the multi-agent translation architecture receiving 710 an application specification in a first format. The specification may encompass various input types, including natural language descriptions, workflow diagrams, flowcharts, or business process models. Once the application specification is received, the specification may be analyzed 720 using a mapping agent within the multi-agent translation architecture to identify functional components. The analysis may include breaking down the application requirements into discrete elements, such as modules, services, or features, which serve as the building blocks for the executable specifications.

[0153] The example method may further include generating 730, using an abstraction agent, abstracted representations of the identified functional components. The abstracted representations may be created in an intermediate format that is independent of specific implementation platforms. The example method may further include determining 740 allocations, using a distribution agent, of the functional components across multiple application environments. The allocation process may be guided by criteria such as efficiency metrics, security requirements, and computational resource availability. The distribution agent may evaluate compatibility between the functional components and the target application environments to ensure optimal performance and resource utilization.

[0154] The method further includes translating 750, using a code generation agent, the abstracted representations into executable specifications. These executable specifications may be configured to be compatible with the determined application environments. The method may output 760 the generated executable specifications through an output interface, enabling their implementation in the target environments.

[0155] Embodiments of the DGAA system may include a marketplace. A marketplace in the context of application development and deployment serves as an exchange where developers can discover, evaluate, and integrate pre-built software components, services, and functionality. Themarketplace facilitates the selection and mapping of one or more functions from a generated application specification to appropriate implementation platforms, services, or components. Rather than requiring organizations to build every aspect of their application from scratch, the marketplace allows them to identify existing implementations that fulfill specific functional requirements identified during the application generation process.

[0156] When an application is generated from high-level specifications, the system identifies discrete functional components that make up the application. The function mapping marketplace then allows developers to selectively map these components to existing implementations, specialized services, or custom development approaches. This flexibility enables optimization of the implementation strategy based on factors such as development resources, performance requirements, security needs, and time constraints. The marketplace may include a repository where providers can publish components that may be accessed by DGAA. Publishing in the context of application development and deployment can encompass a variety of activities aimed at making the application accessible and functional for end-users and other systems. In one example, publishing may include uploading specifications, such as API specifications, which define the protocols and data structures that enable different software components to communicate effectively. In another example, publishing may include providing an application interface. In yet another example, publishing may also include deploying the application to a production environment. Published applications may be accompanied by standardized metadata or other descriptions that define their capabilities, performance characteristics, integration requirements, costs, security guarantees, and the like.

[0157] The marketplace can include a catalog of published applications that includes their descriptions, allowing a comparison of alternatives based on factors such as efficiency, security features, compliance certifications, and compatibility with their target environments.

[0158] As the DGAA system generates an application using the agents, the system may query the marketplace and identify opportunities to implement specific requirements using marketplace functions, suggesting optimal matches based on the functional needs and non-functional requirements of the application. For example, a RAG agent can be employed to query the marketplace catalog to identify available applications and / or functions that can be integrated into the workflow generation process. The RAG agent may query the marketplace's repository of software components, services, and functionalities to locate items that match specific criteria or requirements outlined in the application specification. The RAG agent may use natural language processing and machine learning techniques to interpret the queries and retrieve relevant results from the marketplace catalog. Once the RAG agent identifies potential applications or functions, it canprovide detailed information about each option, including compatibility, performance metrics, and user reviews.

[0159] The results of the RAG queries to the marketplace catalog can be injected into agent conversations as prompts during one or more stages of the workflow generation process. In one example, during the first stages of workflow generation (blueprint generation), the RAG agent's findings can inform the architect agents about potential tools and components that align with the specified business requirements. This information can guide the design of the workflow architecture such that it incorporates the most suitable and efficient solutions available in the marketplace. For example, a query to a marketplace catalog may indicate that one or more workflow functions in a draft blueprint cannot be implemented in available applications which may lead to other agents (e.g., the critic agent) to indicate that other workflow structures should be defined in the blueprint.

[0160] In embodiments, marketplace catalog data may be supplemented with other knowledge across the system and may include usage patterns and performance metrics of functions in similar contexts. The system can evaluate trade-offs between different implementation options, considering factors such as computational efficiency, data locality, and integration complexity to suggest the most appropriate function for each application component.

[0161] In another example, during the second stages of workflow generation (form generation), the RAG agent's findings can inform the architect agents specific components, interfaces, and interactions available to the workflow.

[0162] In one embodiment, marketplace catalog data may be utilized during the translation process from an intermediate representation to a specific application format. As described herein, the translation process involves converting the abstract, platform-independent specifications of the workflow into an executable format tailored to the target environment. By incorporating marketplace catalog data, the system can identify and select the most suitable components and services that align with the workflow's requirements and the capabilities of the target platform.

[0163] In embodiments, a workflow may be mapped to more than one application, allowing for a distributed and optimized execution across multiple applications. This approach may be used to leverage the strengths and capabilities of different applications to handle various aspects of the workflow, ensuring that each component is executed in the most suitable environment. By mapping a workflow to multiple applications, the system can enhance performance, scalability, and resilience, as each application can focus on its specialized tasks.

[0164] In embodiments, the system may query the marketplace catalog data to identify the best- suited applications for different workflow components, considering factors such as functionality, integration capabilities, and performance metrics. In embodiments, various optimization strategiesmay be used to identify complementary functions designed to work together efficiently. The system can recommend function combinations that minimize data transfer between components or suggest alternative implementations better suited to specific deployment environments.

[0165] In one example, a system for function marketplace integration in application development may include a function marketplace interface that provides access to a repository or a catalog of functional components. These components may include pre-built modules, services, or features that can be integrated into an application to enhance its functionality. The marketplace interface may be configured to serve as a centralized resource for discovering and obtaining components that meet specific application requirements.

[0166] The example system may include a plurality of dialectical generative agents that may be configured to work collaboratively to facilitate the selection and mapping of functional components to the application implementation. The agents may include one or more of a requirements analysis agent, a marketplace search agent, a compatibility evaluation agent, and an integration agent.

[0167] The requirements analysis agent may be configured to analyze the application requirements and identify specific functional needs. The marketplace search agent may be configured to query the function marketplace to identify candidate functional components that match the identified needs, generating a list of potential options for integration. Once potential components are identified, the compatibility evaluation agent may be used to assess how well these components align with the application requirements. This evaluation considers factors such as functionality, performance compatibility, and potential integration challenges. The integration agent may be configured for mapping the selected functional components into the application implementation. This process may include configuring the components within the application's architecture.

[0168] The example system may further include an administrative agent. The administrative agent may be configured to manage the process by receiving an application specification that describes the desired application functionality. The administrative agent coordinates dialectical communication between the generative agents, guiding the flow of information and managing task execution. It provides the application specification to the requirements analysis agent, directs the marketplace search agent to locate suitable components, facilitates compatibility evaluations, and instructs the integration agent to complete the implementation process.

[0169] The outcome of the system's process is the generation of an executable application that implements the application specification by utilizing selected functional components from the function marketplace.

[0170] In embodiments, the requirements analysis agent may utilize a large language model to analyze the application specification and generate a detailed breakdown of functional requirements.As described herein, marketplace queries may be executed using RAG agents. In one example, the marketplace search agent may employ RAG techniques to enhance the process of searching for candidate functional components.

[0171] In some embodiments, a monitoring agent is included in the system to track the runtime performance of integrated functional components. The monitoring agent may collect execution metrics and provide feedback that informs future component selection processes.

[0172] In one example, the DGAA system may be used to generate manufacturing and supply chain orchestration applications. An example system may be designed to automatically create intelligent applications that centralize operational data, automate manual tasks, and enhance efficiency within manufacturing environments, particularly in industries such as electronics, semiconductors, and automotive manufacturing.

[0173] The example system may include a plurality of dialectical generative agents configured to receive an input prompt describing specific manufacturing operational needs and generate an output for configuring an intelligent orchestration application. These generative agents may engage in dialectical communication through structured conversation prompts, allowing them to collaborate and refine outputs iteratively. The example system may include an architect agent, that may be configured to create a manufacturing workflow model based on a prompt that includes a description of the manufacturing or supply chain process to be automated. The architect agent generates the initial workflow model and iteratively revises it based on feedback from a review agent, ensuring that the model evolves to meet specific industry standards and operational goals.

[0174] The example system may include a review agent configured to evaluate the manufacturing workflow model by providing detailed feedback that includes a natural language description addressing industry-specific requirements. The example system may include an administrative agent configured to coordinate the entire process by first receiving the input prompt, which is generated using a user input template and provides a natural language description of the manufacturing processes to be standardized and automated. The administrative agent may create a prompt describing the manufacturing workflow to be modeled and deliver this prompt to the architect agent.

[0175] Throughout the iterative development process, the administrative agent facilitates dialectical communication between the generative agents. It manages the continuous exchange of the workflow model between the architect agent and the review agent, promoting the iterative refinement of the model through structured feedback loops. Once the workflow model is thoroughly refined, the administrative agent initiates the generation of at least a portion of the output needed to configure the manufacturing orchestration application. The resulting orchestration application leverages the finalized workflow model to centralize operational data and automate manual tasks.

[0176] In one example, the DGAA system may be used to generate business workflow applications. An example system may be designed to automatically create collaborative workflow applications that centralize business data, automate manual tasks, and enhance cross-departmental collaboration within organizations. An example system may include a plurality of dialectical generative agents configured to receive an input prompt describing specific business operational needs and generate an output for configuring a collaborative workflow application. The generative agents may communicate dialectically through structured conversation prompts, enabling iterative refinement of the workflow model until it meets specific business goals and operational standards. The example system may include an architect agent configured for creating a business workflow model from a prompt that includes a description of the business process to be standardized. The architect agent generates the initial workflow model and iteratively revises it based on feedback provided by a review agent. The review agent may be configured to evaluate the business workflow model and generate feedback that includes a natural language description addressing industryspecific requirements. In one example, the feedback may focus on enhancing cross-functional collaboration and process efficiency, ensuring the workflow model supports communication and productivity across different departments within the organization.

[0177] The example system may include an administrative agent configured to coordinate the process by first receiving the input prompt, which may be generated using a user input template and includes a natural language description of the business processes to be standardized and optimized. The administrative agent may generate a prompt describing the business workflow to be modeled and provide this prompt to the architect agent. Throughout the process, the administrative agent may be configured to manage dialectical communication between the generative agents by iteratively exchanging the business workflow model between the architect agent and the review agent. Once the workflow model is fully developed, the administrative agent may initiate the generation of at least a portion of the output required to configure the collaborative workflow application. The resulting application leverages the finalized workflow model to centralize business data, automate manual tasks, and facilitate effective cross-departmental collaboration.

[0178] Although described primarily in the context of workflow generation, the Dialectical DGAA system demonstrates versatility across various application domains. The principles of dialectical agent interaction, multi-level learning, and abstracted generation can be effectively applied to numerous creative and technical contexts beyond business workflows.

[0179] In one example, the methods and systems described herein may be adapted for music generation applications. In music applications, the process may maintain a three-part process to implement a task. An end user may specify a task to generate music that adapts to the mood of auser. The first part of the process may use agents that include conversations to generate a structure / blueprint of a music piece (i.e., melody, rhythm) and how the elements of the structure may change in response to mood. The agents of the first part may be monitored by an Admin agent that monitors the feedback from generating agents and critics agents during the building of the structure. The first part of the process may include a generation step that generates a machine-readable specification of the structure of the music piece (i.e., MIDI format). The example music-generating process may include a second part in which agents are configured, via an interactive conversation between a generative agent and a critic agent, to generate a description of the specific notes / instruments (elements) that can be used to generate the structure of the music. The descriptions of the notes of the instruments provided to a model that may generate a MIDI representation that may be playable by a computer. The third part of the process may include a data generation step that generates sample mood data for a user. The sample data may be used to simulate or verify and test how the generated music adapts to the mood of a user.

[0180] In another example, the methods and systems described herein may be adapted for game generation applications. In game generation applications, the process may maintain a three-part process to implement a task. An end user may specify a task to generate a game that can teach players about the benefits of saving money. The first part of the process may use agents that include conversations to generate a structure / blueprint of the game. The structure of the game may include basic rules of the game, initial conditions when starting the game, conditions for ending the game, flow of interactions between players, etc. The agents of the first part may be monitored by an Admin agent that monitors the feedback from generating agents and critics agents during the building of the structure. The first part of the process may include a generation step that generates a machine- readable specification of the structure of the game (i.e., a JavaScript code). The example gamegenerating process may include a second part in which agents are configured, via an interactive conversation between a generative agent and a critic agent, to generate a description of the specific visual elements, game pieces, scoring functions, etc., that can be used to generate the structure of the game. The descriptions of the elements may be provided to a model that may generate a JavaScript representation that may be executed on a computer. The third part of the process may include a data generation step that generates sample play data that mimics the actions of a user. The sample data may be used to simulate or verify and test the behavior of the game for different user actions.

[0181] The methods and systems described herein may be deployed in part or in whole through a machine having a computer, computing device, processor, circuit, and / or server that executes computer readable instructions, program codes, instructions, and / or includes hardware configured to functionally execute one or more operations of the methods and systems disclosed herein. The termscomputer, computing device, processor, circuit, and / or server, as utilized herein, should be understood broadly.

[0182] Any one or more of the terms computer, computing device, processor, circuit, and / or server include a computer of any type, capable to access instructions stored in communication thereto such as upon a non-transient computer readable medium, whereupon the computer performs operations of systems or methods described herein upon executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and / or server. Additionally or alternatively, a computer, computing device, processor, circuit, and / or server may be a separate hardware device, one or more computing resources distributed across hardware devices, and / or may include such aspects as logical circuits, embedded circuits, sensors, actuators, input and / or output devices, network and / or communication resources, memory resources of any type, processing resources of any type, and / or hardware devices configured to be responsive to determined conditions to functionally execute one or more operations of systems and methods herein.

[0183] Network and / or communication resources include, without limitation, local area network, wide area network, wireless, internet, or any other known communication resources and protocols. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers include, without limitation, a general purpose computer, a server, an embedded computer, a mobile device, a virtual machine, and / or an emulated version of one or more of these. Example and non-limiting hardware, computers, computing devices, processors, circuits, and / or servers may be physical, logical, or virtual. A computer, computing device, processor, circuit, and / or server may be: a distributed resource included as an aspect of several devices; and / or included as an interoperable set of resources to perform described functions of the computer, computing device, processor, circuit, and / or server, such that the distributed resources function together to perform the operations of the computer, computing device, processor, circuit, and / or server. In certain embodiments, each computer, computing device, processor, circuit, and / or server may be on separate hardware, and / or one or more hardware devices may include aspects of more than one computer, computing device, processor, circuit, and / or server, for example as separately executable instructions stored on the hardware device, and / or as logically partitioned aspects of a set of executable instructions, with some aspects of the hardware device comprising a part of a first computer, computing device, processor, circuit, and / or server, and some aspects of the hardware device comprising a part of a second computer, computing device, processor, circuit, and / or server.

[0184] A computer, computing device, processor, circuit, and / or server may be part of a server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions, and the like. The processor may be or include a signal processor, digital processor, embedded processor, microprocessor, or any variant such as a co-processor (math co-processor, graphic co-processor, communication co-processor and the like) and the like that may directly or indirectly facilitate execution of program code or program instructions stored thereon. In addition, the processor may enable execution of multiple programs, threads, and codes. The threads may be executed simultaneously to enhance the performance of the processor and to facilitate simultaneous operations of the application. By way of implementation, methods, program codes, program instructions and the like described herein may be implemented in one or more threads. The thread may spawn other threads that may have assigned priorities associated with them; the processor may execute these threads based on priority or any other order based on instructions provided in the program code. The processor may include memory that stores methods, codes, instructions, and programs as described herein and elsewhere. The processor may access a storage medium through an interface that may store methods, codes, and instructions as described herein and elsewhere. The storage medium associated with the processor for storing methods, programs, codes, program instructions or other type of instructions capable of being executed by the computing or processing device may include but may not be limited to one or more of a CD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache, and the like.

[0185] A processor may include one or more cores that may enhance speed and performance of a multiprocessor. In embodiments, the process may be a dual core processor, quad core processors, other chip-level multiprocessor and the like that combine two or more independent cores (called a die).

[0186] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer readable instructions on a server, client, firewall, gateway, hub, router, or other such computer and / or networking hardware. The computer readable instructions may be associated with a server that may include a file server, print server, domain server, internet server, intranet server and other variants such as secondary server, host server, distributed server, and the like. The server may include one or more of memories, processors, computer readable transitory and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through a wired or a wireless medium, and the like. The methods, programs, or codes as described herein and elsewhere may be executed by the server. In addition, other devices required for execution of methods as described in this application may be considered as a part of the infrastructure associated with the server.

[0187] The server may provide an interface to other devices including, without limitation, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of instructions across the network. The networking of some or all of these devices may facilitate parallel processing of program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the server through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.

[0188] The methods, program code, instructions, and / or programs may be associated with a client that may include a file client, print client, domain client, internet client, intranet client and other variants such as secondary client, host client, distributed client, and the like. The client may include one or more of memories, processors, computer readable transitory and / or non- transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through a wired or a wireless medium, and the like. The methods, program code, instructions, and / or programs as described herein and elsewhere may be executed by the client. In addition, other devices utilized for execution of methods as described in this application may be considered as a part of the infrastructure associated with the client.

[0189] The client may provide an interface to other devices including, without limitation, servers, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, and the like. Additionally, this coupling and / or connection may facilitate remote execution of methods, program code, instructions, and / or programs across the network. The networking of some or all of these devices may facilitate parallel processing of methods, program code, instructions, and / or programs at one or more locations without deviating from the scope of the disclosure. In addition, all the devices attached to the client through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions to be executed on different devices. In this implementation, the remote repository may act as a storage medium for methods, program code, instructions, and / or programs.

[0190] The methods and systems described herein may be deployed in part or in whole through network infrastructures. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices,routing devices and other active and passive devices, modules, and / or components as known in the art. The computing and / or non-computing device(s) associated with the network infrastructure may include, apart from other components, a storage medium such as flash memory, buffer, stack, RAM, ROM, and the like. The methods, program code, instructions, and / or programs described herein and elsewhere may be executed by one or more of the network infrastructural elements.

[0191] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on a cellular network having multiple cells. The cellular network may either be frequency division multiple access (FDMA) network or code division multiple access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, and the like.

[0192] The methods, program code, instructions, and / or programs described herein and elsewhere may be implemented on or through mobile devices. The mobile devices may include navigation devices, cell phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, electronic books readers, music players, and the like. These mobile devices may include, apart from other components, a storage medium such as a flash memory, buffer, RAM, ROM and one or more computing devices. The computing devices associated with mobile devices may be enabled to execute methods, program code, instructions, and / or programs stored thereon.Alternatively, the mobile devices may be configured to execute instructions in collaboration with other devices. The mobile devices may communicate with base stations interfaced with servers and configured to execute methods, program code, instructions, and / or programs. The mobile devices may communicate on a peer-to-peer network, mesh network, or other communications network. The methods, program code, instructions, and / or programs may be stored on the storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store methods, program code, instructions, and / or programs executed by the computing devices associated with the base station.

[0193] The methods, program code, instructions, and / or programs may be stored and / or accessed on machine readable transitory and / or non-transitory media that may include: computer components, devices, and recording media that retain digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage typically for more permanent storage, such as optical discs, forms of magnetic storage like hard disks, tapes, drums, cards and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage such as CD, DVD; removable media such as flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removablemass storage, off-line, and the like; other computer memory such as dynamic memory, static memory, read / write storage, mutable storage, read only, random access, sequential access, location addressable, file addressable, content addressable, network attached storage, storage area network, bar codes, magnetic ink, and the like.

[0194] Certain operations described herein include interpreting, receiving, and / or determining one or more values, parameters, inputs, data, or other information. Operations including interpreting, receiving, and / or determining any value parameter, input, data, and / or other information include, without limitation: receiving data via a user input; receiving data over a network of any type; reading a data value from a memory location in communication with the receiving device; utilizing a default value as a received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and / or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation, and later updated by a second operation, as part of the receiving a data value. For example, when communications are down, intermittent, or interrupted, a first operation to interpret, receive, and / or determine a data value may be performed, and when communications are restored an updated operation to interpret, receive, and / or determine the data value may be performed.

[0195] Certain logical groupings of operations herein, for example methods or procedures of the current disclosure, are provided to illustrate aspects of the present disclosure. Operations described herein are schematically described and / or depicted, and operations may be combined, divided, reordered, added, or removed in a manner consistent with the disclosure herein. It is understood that the context of an operational description may require an ordering for one or more operations, and / or an order for one or more operations may be explicitly disclosed, but the order of operations should be understood broadly, where any equivalent grouping of operations to provide an equivalent outcome of operations is specifically contemplated herein. For example, if a value is used in one operational step, the determining of the value may be required before that operational step in certain contexts (e.g. where the time delay of data for an operation to achieve a certain effect is important), but may not be required before that operation step in other contexts (e.g. where usage of the value from a previous execution cycle of the operations would be sufficient for those purposes). Accordingly, in certain embodiments an order of operations and grouping of operations as described is explicitly contemplated herein, and in certain embodiments re-ordering, subdivision, and / or different grouping of operations is explicitly contemplated herein.

[0196] The methods and systems described herein may transform physical and / or or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.

[0197] The elements described and depicted herein, including in flow charts, block diagrams, and / or operational descriptions, depict and / or describe specific example arrangements of elements for purposes of illustration. However, the depicted and / or described elements, the functions thereof, and / or arrangements of these, may be implemented on machines, such as through computer executable transitory and / or non-transitory media having a processor capable of executing program instructions stored thereon, and / or as logical circuits or hardware arrangements. Example arrangements of programming instructions include at least: monolithic structure of instructions; standalone modules of instructions for elements or portions thereof; and / or as modules of instructions that employ external routines, code, services, and so forth; and / or any combination of these, and all such implementations are contemplated to be within the scope of embodiments of the present disclosure Examples of such machines include, without limitation, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, electronic books, gadgets, electronic devices, devices having artificial intelligence, computing devices, networking equipment, servers, routers and the like. Furthermore, the elements described and / or depicted herein, and / or any other logical components, may be implemented on a machine capable of executing program instructions. Thus, while the foregoing flow charts, block diagrams, and / or operational descriptions set forth functional aspects of the disclosed systems, any arrangement of program instructions implementing these functional aspects are contemplated herein. Similarly, it will be appreciated that the various steps identified and described above may be varied, and that the order of steps may be adapted to particular applications of the techniques disclosed herein. Additionally, any steps or operations may be divided and / or combined in any manner providing similar functionality to the described operations. All such variations and modifications are contemplated in the present disclosure. The methods and / or processes described above, and steps thereof, may be implemented in hardware, program code, instructions, and / or programs or any combination of hardware and methods, program code, instructions, and / or programs suitable for a particular application. Example hardware includes a dedicated computing device or specific computing device, a particular aspect or component of a specific computing device, and / or an arrangement of hardware components and / or logical circuits to perform one or more of the operations of a method and / or system. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors or other programmable device, along with internal and / or external memory. The processes may also, or instead, be embodied in an application specific integrated circuit, a programmable gate array, programmable array logic, or any other device or combination of devicesthat may be configured to process electronic signals. It will further be appreciated that one or more of the processes may be realized as a computer executable code capable of being executed on a machine readable medium.

[0198] The computer executable code may be created using a structured programming language such as C, an object oriented programming language such as C++, or any other high-level or low- level programming language (including assembly languages, hardware description languages, and database programming languages and technologies) that may be stored, compiled or interpreted to run on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and computer readable instructions, or any other machine capable of executing program instructions.

[0199] Thus, in one aspect, each method described above and combinations thereof may be embodied in computer executable code that, when executing on one or more computing devices, performs the steps thereof. In another aspect, the methods may be embodied in systems that perform the steps thereof, and may be distributed across devices in a number of ways, or all of the functionality may be integrated into a dedicated, standalone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or computer-readable instructions described above. All such permutations and combinations are contemplated in embodiments of the present disclosure.

[0200] While the disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not to be limited by the foregoing examples, but is to be understood in the broadest sense allowable by law.

Claims

CLAIMSWhat is claimed is:

1. A dialectical agent system for automated generation of an application, the system comprising: a plurality of dialectical generative agents configured to receive an input prompt and generate an output for configuring the application, the plurality of dialectical generative agents configured to communicate with each other dialectically via conversation prompts and including: an architect agent configured to create an object from a prompt including a description of an object to be generated, and to iteratively revise the object based on feedback about the object from a review agent; a review agent configured to receive the object and to generate the feedback about the object, wherein the feedback includes a natural language description; and an administrative agent configured to: receive the input prompt, wherein the input prompt is generated with a user input template and includes a natural language description of the application; generate the prompt including the description of the object to be generated and provide the prompt including the description of the object to be generated to the architect agent; provide dialectical communication between the dialectical generative agents, including to: iteratively provide the object created by the architect agent to the review agent, and the feedback generated by the review agent about the object to the architect agent; and initialize generation of at least a portion of the output for configuring the application based on the object.

2. The system of claim 1 , further comprising: a machine formatting agent to generate the at least the portion of the output for configuring the application, the at least the portion of the output including a machine-readable description.

3. The system of claim 2, wherein the machine-readable description is formatted in at least one of JavaScript object notation (JSON), extensible markup language (XML), or Python.

4. The system of claim 1 , further comprising: an abstraction layer to provide a machine-readable description for the application based on the object.

5. The system of claim 1, wherein at least two of the dialectical generative agents in the plurality of dialectical generative agents communicate using conversation prompts.

6. The system of claim 1 , wherein the application is for a workflow and the natural language description of the application to be generated includes a description of the workflow.

7. The system of claim 1, wherein each of the dialectical generative agents of the plurality of dialectical generative agents includes a separate large language model (LLM).

8. The system of claim 1, wherein the administrative agent iteratively provides the object created by the architect agent to the review agent, and the feedback generated by the review agent about the object to the architect agent, until the administrative agent observes a stopping criteria.

9. The system of claim 8, wherein the administrative agent initializes generation of the at least the portion of the output for configuring the application upon observing the stopping criteria.

10. The system of claim 1, wherein the administrative agent is further configured to: select which dialectical generative agent of the plurality of dialectical generative agents to analyze an output of another dialectical generative agent of the plurality of dialectical generative agents.

11. The system of claim 1, wherein the administrative agent is further configured to: permit a human user to override or monitor decisions of the administrative agent.

12. The system of claim 1, wherein the administrative agent is further configured to: identify and initialize the dialectical generative agents of the plurality of dialectical generative agents.

13. The system of claim 1, wherein the plurality of dialectical generative agents further includes a retrieval- augmented generation (RAG) agent configured to provide specific data related to the object based on at least one of an external knowledge store or platform data.

14. The system of claim 1, wherein one or more of the dialectical generative agents of the plurality of dialectical generative agents is initialized with a prompt instructing the one or more of the dialectical generative agents of its purpose and configuration.

15. The system of claim 1, wherein one or more of the dialectical generative agents of the plurality of dialectical generative agents executes iteratively and includes at least three levels of learning.

16. The system of claim 1, wherein the application is at least one of: a static application having a fixed architecture and a fixed set of functionalities; or a dynamic application configured to be dynamically updated based on at least one of a user interaction, a data input, or an external event.

17. The system of claim 1, further comprising a tripartite architecture including at least three parts, each of the at least three parts including a respective plurality of dialectical generative agents, the object of a first part of the at least three parts being a blueprint for the application, the object of a second part of the at least three parts being elements for implementing the architecture, and the object of a third part of the at least three parts being data for the architecture or the elements.

18. The system of claim 17, wherein: the application is for a business workflow and the input prompt generated with the user input template includes a natural language description of the business workflow to be implemented by the application; the plurality of dialectical generative agents further includes a retrieval-augmented generation (RAG) agent; and the first part includes a first plurality of dialectical generative agents to generate the blueprint of the application, including: the architect agent including a first architect agent configured to create the blueprint of the application, and to iteratively revise the blueprint based on feedback about the blueprint from a first review agent;the RAG agent including a first architect RAG agent configured to query data related to the business workflow, the first architect RAG agent configured to identify the data related to the business workflow in at least one of an external knowledge store or platform data, wherein the first architect agent utilizes the data related to the business workflow to create the blueprint of the application; the review agent including the first review agent configured to receive, as an input prompt, the blueprint of the application and to generate the feedback about the blueprint of the application, wherein the feedback includes a natural language description; the RAG agent further including a first review RAG agent configured to query data related to the blueprint, the first review RAG agent configured to identify the data related to the blueprint in at least one of the external knowledge store or the platform data, wherein the first review agent utilizes the data related to the blueprint to generate the feedback about the blueprint of the application; and the administrative agent including a first administrative agent configured to iteratively provide the blueprint to the first review agent and the feedback generated by the first review agent about the blueprint to the first architect agent.

19. The system of claim 18, wherein: the second part includes a second plurality of dialectical generative agents to generate a form description for the application, including: the architect agent including a second architect agent configured to be prompted with the blueprint, to create the form description for the application using the blueprint, and to iteratively revise the form description based on feedback about the form description from a second review agent; the RAG agent including a second architect RAG agent, wherein the second architect agent utilizes data queried by the second architect RAG agent to create the form description; the review agent including the second review agent configured to receive, as an input prompt, the form description for the application and to generate the feedback about the form description for the application, wherein the feedback includes a natural language description; the RAG agent further including a second review RAG agent, wherein the second review agent utilizes data queried by the second review RAG agent to generate the feedback about the form description for the application; and the administrative agent including a second administrative agent configured to iteratively provide the form description to the second review agent and the feedbackgenerated by the second review agent about the form description to the second architect agent.

20. The system of claim 19, wherein: the third part includes a third plurality of dialectical generative agents to generate a data description of sample data for the form description, including: the architect agent including a third architect agent configured to be prompted with the form description, to create the data description of sample data for the form description using the form description, and to iteratively revise the data description based on feedback about the data description from a third review agent; the RAG agent including a third architect RAG agent, wherein the third architect agent utilizes data queried by the third architect RAG agent to create the data description; the review agent including the third review agent configured to receive, as an input prompt, the data description of sample data and to generate the feedback about the data description of sample data, wherein the feedback includes a natural language description; the RAG agent further including a third review RAG agent, wherein the third review agent utilizes data queried by the third review RAG agent to generate the feedback about the data description of the sample data; and the administrative agent including a third administrative agent configured to iteratively provide the data description to the third review agent and the feedback generated by the third review agent about the data description to the third architect agent.

21. The system of claim 20, wherein: the first administrative agent initializes generation of the portion of the output including a machine-readable representation of the blueprint; the second administrative agent initializes generation of the portion of the output including a machine-readable representation of the form description; and the third administrative agent initializes generation of the portion of the output including a machine-readable representation of the data description of the sample data.

22. The system of claim 21, wherein the first, second, and third administrative agents initialize generation of the portions of the output including the machine-readable representations by providing the blueprint, the form description, and the data description to at least one machine formatting agent.

23. The system of claim 22, wherein the first, second, and third administrative agents provide the blueprint, the form description, and the data description to respective machine formatting agents of the first, second, and third parts.

24. The system of claim 23, wherein the machine formatting agent includes a table to JSON agent.

25. A system, comprising: a tripartite dialectical multi-agent architecture to implement a task in an application specified by a user, including: a first part including a first plurality of dialectical agents, a second part including a second plurality of dialectical agents, and a third part including a third plurality of dialectical agents, the first plurality of dialectical agents configured to conduct a conversation with each other to generate a description of an architecture to implement the task, the second plurality of dialectical agents configured to conduct a conversation with each other to define a description of elements for implementing the architecture, and the third plurality of dialectical agents configured to conduct a conversation with each other to define a description of data for the architecture or the elements.

26. The system of claim 25, wherein the description of the architecture to implement the task includes at least one of a listing of elements, a timing of the elements, an organization of the elements, or a constraint on the elements.

27. The system of claim 25, wherein the description of the elements for implementing the architecture includes at least one of a detail of each element, an alternative implementation of each element, a specification of how the elements interact, or a dependency between the elements.

28. The system of claim 26, wherein the description of the data for the architecture or the elements includes at least one of a description of data sets for implementing, testing, or verifying an implementation of the task generated by the description of the architecture and the elements.

29. The system of claim 25, further comprising at least one machine formatting agent configured to generate a machine-readable representation of at least one of the description of the architecture, the description of the elements, or the description of the data, for the application.

30. The system of claim 29, wherein the at least one machine formatting agent includes a first machine formatting agent included in the first plurality of dialectical agents, a second machine formatting agent included in the second plurality of dialectical agents, and a third machine formatting agent included in the third plurality of dialectical agents.

31. The system of claim 29, further comprising: an abstraction layer configured to generate the machine-readable representation in an application-specific format for executing the application.

32. The system of claim 26, wherein the first plurality of dialectical agents, the second plurality of dialectical agents, and the third plurality of dialectical agents each includes a plurality of trained large language models (LLMs) corresponding to at least some of the agents.

33. The system of claim 32, wherein the first plurality of dialectical agents, the second plurality of dialectical agents, and the third plurality of dialectical agents each includes at least one retrieval- augmented generation (RAG) agent configured to provide specific data based on at least one of an external knowledge store or platform data.

34. The system of claim 26, wherein the task is a process flow, the description of the architecture includes an application blueprint, the elements include one or more forms for the application, and the data includes one or more sample data for the one or more forms.

35. The system of claim 34, further comprising at least one machine formatting agent configured to generate a machine-readable representation of the application blueprint, the one or more forms for the application, and the one or more sample data for the one or more forms.

36. A system for post-deployment application evolution, the system comprising: a monitoring infrastructure integrated within a deployed application, the monitoring infrastructure configured to track user interactions and data inputs within the application;a plurality of dialectical generative agents configured to analyze monitored data and implement application modifications, the plurality of dialectical generative agents including: a pattern recognition agent configured to analyze the tracked user interactions and data inputs to identify usage patterns, inefficiencies, and enhancement opportunities; a suggestion agent configured to generate modification proposals based on the identified patterns, wherein the proposals include natural language descriptions of recommended changes; an impact analysis agent configured to evaluate potential effects of the modification proposals on existing application functionality; an implementation agent configured to translate approved modification proposals into executable code changes; an administrative agent configured to: coordinate dialectical communication between the generative agents, including to: provide collected monitoring data to the pattern recognition agent; iteratively facilitate refinement of the modification proposals through dialogue between the pattern recognition agent and the suggestion agent; direct the impact analysis agent to assess proposal feasibility; and instruct the implementation agent to execute approved modifications; and implement the application modifications while maintaining application availability, wherein the modifications improve application functionality based on actual usage patterns without requiring manual redevelopment.

37. The system of claim 36, wherein the monitoring infrastructure includes configurable tracking parameters that adjust detail and scope of tracked user interactions based on observed application usage patterns.

38. The system of claim 36, wherein the suggestion agent is configured to prioritize modification proposals based on predicted impact on user efficiency, computational resource utilization, and alignment with business objectives.

39. The system of claim 36, wherein the impact analysis agent utilizes a simulation environment to test proposed modifications against historical usage data before implementation.

40. The system of claim 36, wherein the implementation agent incorporates feature flagging to selectively expose modifications to subsets of users before full deployment.

41. The system of claim 36, further comprising a marketplace interface that identifies third-party applications, services, or agents that could enhance application functionality based on identified usage patterns.

42. The system of claim 41, wherein the administrative agent is further configured to evaluate third- party offerings from the marketplace and recommend integration points within the application.

43. The system of claim 36, wherein the system maintains an abstraction layer between the functional representation of the application and the executable implementation of the application to facilitate modifications across different execution platforms.

44. The system of claim 36, wherein the dialectical generative agents operate on an internal document representation of the application that can be exported, manually modified, and reimported to implement human- guided changes alongside automated modifications.

45. A system for automated post-deployment update of applications, the system comprising: a monitoring agent configured to track user interactions and data inputs within an application, wherein the tracking produces tracked user interactions and tracked data inputs; a pattern recognition agent configured to analyze the tracked user interactions and the tracked data inputs to identify patterns and trends, wherein the pattern recognition agent produces an analysis result; a suggestion agent configured to receive the analysis result and generate modification suggestions, wherein the modification suggestions include a natural language description; an administrative agent configured to: oversee a monitoring process, wherein the monitoring process is initiated with a predefined configuration and includes a natural language description of functionalities of the application; and facilitate communication by iteratively providing the analysis result generated by the pattern recognition agent to the suggestion agent, and the modification suggestions generated by the suggestion agent to the pattern recognition agent; and a dialectical agent system configured to implement at least a portion of the modification suggestions to the application based on the natural language description from the suggestion agent.

46. A method for post-deployment application evolution, the method comprising: tracking, via monitoring infrastructure, user interactions and data inputs within an application; analyzing, via a plurality of dialectical generative agents, monitored data to identify application modification opportunities, wherein the analyzing includes: analyzing, via a pattern recognition agent, the tracked user interactions, and data inputs to identify usage patterns, inefficiencies, and enhancement opportunities; generating, via a suggestion agent, modification proposals based on the identified patterns, wherein the proposals include natural language descriptions of recommended changes; evaluating, via an impact analysis agent, potential effects of the modification proposals on existing application functionality; and translating, via an implementation agent, approved modification proposals into executable code changes; coordinating, via an administrative agent, dialectical communication between the generative agents, including: providing collected monitoring data to the pattern recognition agent; iteratively facilitating refinement of modification proposals through dialogue between the pattern recognition agent and the suggestion agent; directing the impact analysis agent to assess proposal feasibility; and instructing the implementation agent to execute approved modifications; and implementing the application modifications while maintaining application availability, wherein the modifications improve application functionality based on actual usage patterns without requiring manual redevelopment.

47. The system of claim 45, wherein the monitoring agent is configured to track mouse clicks, keyboard inputs, and touch gestures.

48. The system of claim 45, wherein the pattern recognition agent is further configured to determine unused features of the application by analyzing frequency and duration of the user interactions with application functionalities.

49. The system of claim 45, wherein the suggestion agent is configured to generate modification suggestions that include changes to data input types and an order of operations within the application.

50. The system of claim 45, wherein the pattern recognition agent is designed to recognize specific patterns related to user behavior, such as frequent errors or common navigation paths.

51. The system of claim 45, wherein the suggestion agent incorporates user feedback to refine and personalize modification suggestions.

52. The system of claim 45, wherein the monitoring agent is configured to categorize data inputs based on their source and type.

53. A system for translating application specifications into executable specifications, the system comprising: a processing unit; and a memory storage device including instructions that when executed by the processing unit cause the system to: receive an application specification in a first format; process the application specification using a multi-agent translation architecture, wherein the multi-agent translation architecture includes: a mapping agent configured to analyze the application specification and identify functional components; an abstraction agent configured to generate abstracted representations of the identified functional components; a distribution agent configured to determine an allocation of the functional components across multiple application environments based on at least one of efficiency metrics, security requirements, or computational resource availability; and a code generation agent configured to translate the abstracted representations into executable specifications compatible with the determined application environments; and output the executable specifications for implementation.

54. A system for translating application specifications into executable specifications, the system comprising: a multi-agent translation architecture configured to receive an application specification in a first format and generate executable specifications, the multi-agent translation architecture including: a mapping agent configured to analyze the application specification and identify functional components;an abstraction agent configured to generate abstracted representations of the identified functional components; a distribution agent configured to determine an allocation of the functional components across multiple application environments based on at least one of efficiency metrics, security requirements, or computational resource availability; and a code generation agent configured to translate the abstracted representations into executable specifications compatible with the determined application environments; and an output interface configured to provide the executable specifications for implementation.

55. The system of claim 54, wherein the application specification comprises at least one of a natural language description, a workflow diagram, a flowchart, or a business process model.

56. The system of claim 54, wherein the abstracted representations comprise an intermediate representation format that is independent of specific implementation platforms.

57. The system of claim 54, wherein the multi-agent translation architecture further includes a requirements validation agent configured to verify completeness and consistency of the application specification before processing by the mapping agent.

58. The system of claim 54, wherein the distribution agent evaluates compatibility between functional components and application environments to determine allocation.

59. The system of claim 54, wherein the system maintains a knowledge base of previously translated specifications to improve translation accuracy for new application specifications.

60. The system of claim 54, wherein the code generation agent is configured to generate executable specifications for multiple target platforms from the abstracted representations.

61. The system of claim 54, wherein the system includes a marketplace interface configured to recommend third-party services or components that could implement specific functional components.

62. A method for translating application specifications into executable specifications, the method comprising:receiving, by a multi-agent translation architecture, an application specification in a first format; analyzing, by a mapping agent of the multi-agent translation architecture, the application specification to identify functional components; generating, by an abstraction agent of the multi-agent translation architecture, abstracted representations of the identified functional components; determining, by a distribution agent of the multi-agent translation architecture, an allocation of the functional components across multiple application environments based on at least one of efficiency metrics, security requirements, or computational resource availability ; translating, by a code generation agent of the multi-agent translation architecture, the abstracted representations into executable specifications compatible with the determined application environments; and providing, via an output interface, the executable specifications for implementation.

63. A dialectical agent system for automated generation of an application, the system comprising: a plurality of dialectical generative agents configured to receive an input prompt and generate an output for configuring the application, the plurality of dialectical generative agents configured to communicate with each other dialectically via conversation prompts and including: an architect agent configured to create an object from a prompt including a description of an object to be generated, and to iteratively revise the object based on feedback about the object from a review agent; a review agent configured to receive the object and to generate the feedback about the object, wherein the feedback includes a natural language description; and an administrative agent configured to: receive the input prompt, wherein the input prompt is generated with a user input template and includes a natural language description of the application; generate the prompt including the description of the object to be generated and provide the prompt including the description of the object to be generated to the architect agent; provide dialectical communication between the dialectical generative agents, including to: iteratively provide the object created by the architect agent to the review agent, and the feedback generated by the review agent about the object to the architect agent; andinitialize generation of at least a portion of the output for configuring the application based on the object; coordinate three levels of learning processes, including: a first-level learning process involving collaboration among the dialectical generative agents to generate an initial workflow output from the input prompt, wherein the administrative agent manages communication and iterative refinement of the output through feedback mechanisms; a second-level learning process wherein the administrative agent customizes the initial workflow output based on industry-specific requirements and historical data; and a third-level learning process wherein the administrative agent integrates human feedback into the workflow generation process.

64. The dialectical agent system of claim 63, wherein the first-level learning process comprises: a shared context repository accessible to dialectical generative agents.

65. The dialectical agent system of claim 63, wherein the second-level learning process comprises: maintaining a knowledge base of industry-specific terminology, workflows, and best practices; and identifying common structures, components, and relationships that recur in applications.

66. The dialectical agent system of claim 63, wherein the third-level learning process comprises: recording human modifications to generated applications; analyzing patterns in human edits to identify systematic preferences and corrections; distinguishing between domain-specific preferences and universal improvements; and integrating identified patterns into the first- level and second-level learning.

67. A method for automated generation of an application using dialectical generative agents, the method comprising: receiving, by an administrative agent, an input prompt, wherein the input prompt is generated with a user input template and includes a natural language description of the application; generating, by the administrative agent, a prompt including a description of an object to be generated;providing, by the administrative agent, the prompt including the description of the object to an architect agent; creating, by the architect agent, the object from the prompt; providing, by the administrative agent, the object created by the architect agent to a review agent; generating, by the review agent, feedback about the object, wherein the feedback includes a natural language description; providing, by the administrative agent, the feedback generated by the review agent to the architect agent; iteratively revising, by the architect agent, the object based on the feedback; iteratively repeating the providing of the revised object to the review agent and the feedback to the architect agent; initializing, by the administrative agent, generation of at least a portion of an output for configuring the application based on the object; coordinating, by the administrative agent, three levels of learning processes, including: implementing a first-level learning process involving collaboration among the dialectical generative agents to generate an initial workflow output from the input prompt, wherein the administrative agent manages communication and iterative refinement of the output through feedback mechanisms; implementing a second-level learning process to customize the initial workflow output based on industry-specific requirements and historical data; and implementing a third-level learning process to integrate human feedback into the workflow generation process.

68. A dialectical agent system for automated generation of manufacturing and supply chain orchestration applications, the system comprising: a plurality of dialectical generative agents configured to receive an input prompt describing manufacturing operational needs and generate an output for configuring an intelligent orchestration application, the plurality of dialectical generative agents configured to communicate with each other dialectically via conversation prompts and including: an architect agent configured to create a manufacturing workflow model from a prompt including a description of a manufacturing or supply chain process to be automated, and to iteratively revise the workflow model based on feedback about the model from a review agent;a review agent configured to receive the manufacturing workflow model and to generate the feedback about the model, wherein the feedback includes a natural language description addressing industry-specific requirements for electronics, semiconductor, or automotive manufacturing; and an administrative agent configured to: receive the input prompt, wherein the input prompt is generated with a user input template and includes a natural language description of manufacturing processes to be standardized and automated; generate the prompt including the description of the manufacturing workflow to be modeled and provide the prompt including the description of the manufacturing workflow to the architect agent; provide dialectical communication between the dialectical generative agents to iteratively provide the manufacturing workflow model created by the architect agent to the review agent, and the feedback generated by the review agent about the model to the architect agent; and initialize generation of at least a portion of the output for configuring the manufacturing orchestration application based on the workflow model, wherein the application centralizes operational data, automates manual tasks.

69. A dialectical agent system for automated generation of business workflow applications, the system comprising: a plurality of dialectical generative agents configured to receive an input prompt describing business operational needs and generate an output for configuring a collaborative workflow application, the plurality of dialectical generative agents configured to communicate with each other dialectically via conversation prompts and including: an architect agent configured to create a business workflow model from a prompt including a description of a business process to be standardized, and to iteratively revise the workflow model based on feedback about the model from a review agent; a review agent configured to receive the business workflow model and to generate the feedback about the model, wherein the feedback includes a natural language description addressing industryspecific requirements for cross-functional collaboration and process efficiency; and an administrative agent configured to: receive the input prompt, wherein the input prompt is generated with a user input template and includes a natural language description of business processes to be standardized and optimized;generate the prompt including the description of the business workflow to be modeled and provide the prompt including the description of the business workflow to the architect agent; provide dialectical communication between the dialectical generative agents to iteratively provide the business workflow model created by the architect agent to the review agent, and the feedback generated by the review agent about the model to the architect agent; and initialize generation of at least a portion of the output for configuring the collaborative workflow application based on the workflow model, wherein the application centralizes business data, automates manual tasks, and facilitates cross-departmental collaboration.

70. A system for function marketplace integration in application development, the system comprising: a function marketplace interface configured to provide access to a repository of functional components; a plurality of dialectical generative agents configured to facilitate selection and mapping of functional components to an application implementation, the plurality of dialectical generative agents including: a requirements analysis agent configured to analyze application requirements and identify functional needs; a marketplace search agent configured to query the function marketplace and identify candidate functional components that satisfy the identified functional needs; a compatibility evaluation agent configured to assess compatibility between the candidate functional components and the application requirements; and an integration agent configured to map selected functional components into the application implementation; and an administrative agent configured to: receive an application specification describing desired application functionality; coordinate dialectical communication between the generative agents, including to: provide the application specification to the requirements analysis agent; direct the marketplace search agent to locate candidate functional components based on the identified functional needs; facilitate evaluation of the candidate functional components by the compatibility evaluation agent; and instruct the integration agent to incorporate selected functional components into the application implementation; andgenerate an executable application that implements the application specification using the selected functional components from the function marketplace.

71. The system of claim 70, wherein the requirements analysis agent uses a large language model to analyze the application specification and generate a detailed functional requirements breakdown.

72. The system of claim 70, wherein the marketplace search agent employs retrieval-augmented generation (RAG) to enhance the search for candidate functional components.

73. The system of claim 70, wherein the administrative agent maintains a historical record of component selections and integrations to support iterative learning and optimization.

74. The system of claim 70, wherein the dialectical communication between agents includes: exchange of conversational prompts, collaborative refinement of component selections, and iterative validation of component compatibility.

75. The system of claim 70, further comprising a monitoring agent configured to track runtime performance of integrated functional components and provide feedback for future selection processes.

76. A system for agent- integrated application deployment, the system comprising: a plurality of dialectical generative agents configured to generate an application with built- in task automation, the plurality of dialectical generative agents including: an analysis agent configured to evaluate an application specification and identify tasks within the application suitable for automation, wherein the identified tasks are iteratively refined based on feedback; a framework architect agent configured to design an agent integration framework for the application based on the identified tasks, wherein the framework enables runtime deployment of automation agents within the application; a code generation agent configured to generate application code that implements functionality of the application and the agent integration framework; and an administrative agent configured to: receive an input specification describing application requirements;coordinate dialectical communication between the generative agents, including to provide the application specification to the analysis agent for task identification; iteratively facilitate information exchange between the analysis agent and the framework architect agent to optimize the agent integration framework; and direct the code generation agent to incorporate the agent integration framework into the application code; and deploy the application with the agent integration framework, wherein the framework: enables runtime instantiation of automation agents within the application; provides communication channels between the application and the automation agents; and configures the automation agents to execute identified tasks within the application.

77. The system of claim 76, wherein the framework architect agent generates a graph-based architecture that defines interaction pathways and dependency rules for automation agents within the application.

78. The system of claim 76, further comprising a monitoring agent configured to: track performance of automation agents; collect runtime execution metrics; and provide feedback to refine future task automation strategies.

79. The system of claim 76, wherein the code generation agent dynamically creates adapter interfaces that enable integration of external automation agents into the application framework.

80. The system of claim 76, wherein the agent integration framework includes: a resource allocation mechanism; and fallback strategies for agent execution failures.

81. The system of claim 76, wherein the analysis evaluates tasks based on repetitiveness, structured input-output patterns, and computational.

82. The system of claim 76, wherein the framework architect agent implements a modular agent framework that is configured for dynamic addition of new automation capabilities without modifying core application code.

83. The system of claim 76, wherein the administrative agent implements a progressive automation schedule that increases scope of automated tasks based on successful performance metrics and user feedback.

84. A method for agent- integrated application deployment, the method comprising: receiving, by an administrative agent, an input specification describing application requirements; evaluating, by an analysis agent, an application specification to identify tasks within the application suitable for automation; iteratively refining the identified tasks based on feedback; designing, by a framework architect agent, an agent integration framework for the application based on the identified tasks, wherein the framework enables runtime deployment of automation agents within the application; coordinating, by the administrative agent, dialectical communication between generative agents, including: providing the application specification to the analysis agent for task identification; iteratively facilitating information exchange between the analysis agent and the framework architect agent to optimize the agent integration framework; and directing a code generation agent to incorporate the agent integration framework into application code; generating, by the code generation agent, the application code that implements functionality of the application and the agent integration framework; and deploying the application with the agent integration framework, wherein the framework: enables runtime instantiation of automation agents within the application; provides communication channels between the application and the automation agents; and configures the automation agents to execute identified tasks within the application.

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