AI agent construction system based on vector knowledge base and large model workflow

Abstract

The invention discloses an AI agent construction system based on a vector knowledge base and large model workflow, and belongs to the technical field of process management. The problem that the whole process of workflow scheduling and quality control cannot be managed smoothly due to the fact that an existing AI system cannot assemble heterogeneous and unstructured domain knowledge and business rules dispersed in internal documents, databases, APIs and expert experience through a standardized and low-code platform in a rapid and high-quality mode is solved. Comprising a project initialization module, a knowledge base configuration module, a model service module, a workflow arrangement module, a test verification module and a deployment monitoring module. According to the method, data, knowledge, reasoning and quality control are communicated through a unified vector knowledge base and a low-code drag workflow, a dynamic hypergraph is driven by using computable information potential energy / cognitive entropy, automatic topology operation cutting circulation and shortcut establishment are performed before deadlock outbreak, and predictable hard constraint management of knowledge fusion, process arrangement and cost process is realized.

Description

Technical Field

[0001] This invention relates to the field of process management technology, and in particular to an AI agent construction system based on vector knowledge base and large model workflow. Background Technology

[0002] With the continuous iteration of generative artificial intelligence technology and the explosive development of large language models supported by algorithm optimization and computing power, intelligent agents with autonomous perception, planning, execution, and feedback capabilities have gradually become the core form for AI technology to move from theory to industrial application, showing broad application prospects in multiple fields such as finance, manufacturing, and government. In contrast, traditional AI systems, limited by their technical architecture and design logic, can often only complete single-round question-and-answer interactions or fixed task processing under preset processes. They lack the ability to deeply integrate cross-industry and multi-source knowledge, cannot integrate structured and unstructured data scattered across different systems, and lack the ability to flexibly arrange complex domain logic. They are difficult to adapt to dynamically changing business scenarios and lack adaptive decision-making mechanisms based on real-time feedback. They cannot adjust execution strategies according to variables in the task process. These shortcomings make it difficult for traditional AI systems to form closed-loop execution capabilities when facing highly complex tasks in enterprise-level scenarios such as full-process project management, multi-dimensional risk control, and cross-departmental collaborative decision-making, and they cannot meet the needs of enterprises for efficient, intelligent, and flexible digital transformation.

[0003] However, current traditional AI systems have the following shortcomings:

[0004] The knowledge sources are too generalized: the training relies solely on general parameters from the Internet and lacks in-depth text corpora from vertical industries.

[0005] Limited semantic retrieval performance: Most systems use keyword retrieval and cannot perform deep semantic matching;

[0006] Fragmented workflow scheduling: Model calls, logical judgments, and external API interactions are not integrated into a visually visible and programmable process;

[0007] Lack of quality control: The output of large models is uncontrollable, posing risks of illusion and compliance.

[0008] Therefore, current AI systems are unable to quickly and efficiently assemble heterogeneous and unstructured domain knowledge and business rules scattered in internal documents, databases, APIs, and expert experience through a standardized, low-code platform, which makes it impossible to manage the entire process of workflow scheduling and quality control smoothly.

[0009] Therefore, there is an urgent need for an AI agent construction system to achieve full-process management from knowledge access, semantic vectorization, inference service configuration to workflow scheduling and quality control.

[0010] Therefore, an AI agent construction system based on vector knowledge base and large model workflow is proposed to solve or alleviate the above problems. Summary of the Invention

[0011] The purpose of this invention is to address the shortcomings of existing technologies by proposing an AI agent construction system based on vector knowledge base and large model workflow.

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] The AI ​​agent construction system based on vector knowledge base and large model workflow includes a project initialization module, a knowledge base configuration module, a model service module, a workflow orchestration module, a testing and verification module, and a deployment and monitoring module.

[0014] The output of the project initialization module is connected to the knowledge base configuration module and the model service module, which is used to create intelligent agent projects and set the basic information of intelligent agents.

[0015] The output of the knowledge base configuration module is connected to the workflow orchestration module, which is used to access multi-source data for vectorization processing and to configure the prompt word library and rule library.

[0016] The output of the model service module is connected to the workflow orchestration module, which is used to configure the core inference service stack for domain-specific fine-tuning.

[0017] The output of the workflow orchestration module is connected to the test and verification module, which is used to design workflows for resource orchestration and deployment.

[0018] The output of the test and verification module is connected to the deployment and monitoring module, which is used to test the submitted and orchestrated agent workflow. The deployment and monitoring module is used to deploy the production environment and configure the monitoring and alarm system.

[0019] Preferably, the project initialization module includes a web management console, a project configurator, and a metadata storage. The web management console is connected to the project configurator to receive user-inputted agent project configuration information and transmit it to the project configurator. The project configurator is connected to the metadata storage to encapsulate the received configuration information into structured metadata and store it in the metadata storage for use by other modules in the system.

[0020] Preferably, the knowledge base configuration module includes a multi-source data access device, a document loader, an embedding model, a vector database, a prompt word library manager, and a rule base manager. The multi-source data access device is connected to the document loader to transmit the accessed multi-source data to the document loader for parsing. The document loader is connected to the embedding model to transmit the parsed text to the embedding model for vectorization processing. The embedding model is connected to the vector database to store the generated vector data in the vector database and establish an index. The prompt word library manager and the rule base manager are configured in parallel to provide the assembled prompt words and the loaded business compliance rules, respectively.

[0021] Preferably, the model service module includes an inference service configurator, a vLLM inference engine, a gRPC interface service, and a LoRA fine-tuning pipeline. The inference service configurator connects to and controls the vLLM inference engine to configure high-performance inference parameters and start the service. The output of the inference service configurator is connected to the gRPC interface service, which is connected to the inference service configurator to receive model service instances forwarded by the inference service configurator and provide high-concurrency, low-latency inference APIs. The input of the LoRA fine-tuning pipeline is used to access the domain dataset, and the output of the LoRA fine-tuning pipeline is connected to the inference service configurator. The LoRA fine-tuning pipeline is used to perform domain-specific fine-tuning on the basic model and provide the fine-tuned model version and parameters to the inference service configurator for loading and updating.

[0022] Preferably, the workflow orchestration module includes a visual workflow designer, a workflow engine, a Kubernetes orchestrator, and a task executor. The visual workflow designer is connected to the workflow engine to provide a graphical interface for orchestrating intelligent agent workflows containing multiple logical steps and sending the orchestrated workflow definition to the workflow engine. The workflow engine is connected to the visual workflow designer, the Kubernetes orchestrator, and the task executor to parse the received workflow definition and convert it into a task sequence organized by a directed acyclic graph, driving the task executor to execute each task sequentially. The Kubernetes orchestrator is connected to the workflow engine to receive resource requests scheduled by the workflow engine and dynamically allocate and schedule GPU nodes and CPU nodes. The GPU nodes are dedicated to executing model inference tasks, and the CPU nodes are dedicated to executing data retrieval and processing tasks and implementing automatic resource scaling and secure communication with the service mesh. The task executor is connected to the workflow engine and external services to specifically execute various types of tasks issued by the workflow engine. These tasks include input parsing, knowledge retrieval, prompt word assembly, model invocation, quality review, and response output.

[0023] Preferably, the test verification module includes an end-to-end test engine, a quality evaluator, and a test report generator. The end-to-end test engine is connected to the workflow orchestration module to receive the workflow definition of the agent under test and simulate real user requests to initiate end-to-end functional tests. The output of the end-to-end test engine is connected to the quality evaluator to send the raw output and process data generated during the test to the quality evaluator. The quality evaluator is connected to the end-to-end test engine and the test report generator to receive test data and perform professional quality assessments, including accuracy assessment, timeliness verification, compliance checks, and response speed tests. The output of the quality evaluator is connected to the test report generator to send the quantitative assessment results and problem details to the test report generator. The test report generator is connected to the quality evaluator and the downstream deployment monitoring module to receive the assessment results, generate a structured test report containing a comprehensive quality score, detailed test item results, and improvement suggestions, and output the verified agent workflow and its quality endorsement to the deployment monitoring module for production deployment.

[0024] Preferably, the deployment monitoring module includes a deployment manager, a monitoring data collector, an alarm engine, an operations and maintenance console, and a production cluster. The deployment manager connects to the test and verification module and the production cluster. The deployment manager receives verified agent workflows and deploys them to the specified production cluster environment according to preset resource quotas, API gateway and load balancing configuration strategies. The monitoring data collector connects to agent service instances in the production cluster to continuously collect multi-dimensional runtime data, including model call latency, retrieval time, GPU utilization and business metrics. The output of the monitoring data collector is connected to the alarm engine. The alarm engine connects to the monitoring data collector and the operations and maintenance console to receive the collected monitoring data, perform real-time analysis according to predefined threshold rules, and generate and send alarm information when anomalies or performance bottlenecks are detected. The operations and maintenance console connects to the alarm engine. The operations and maintenance console provides a unified graphical interface for operations and maintenance personnel to view system health status, manage deployment tasks, configure alarm rules, and perform manual intervention.

[0025] Preferably, it also includes a data acquisition and injection layer module, a core computing and modeling layer module, a decision-making and intervention layer module, and a support and interface module;

[0026] The data acquisition and injection layer module acquires the raw dialogue data and task target data between intelligent agents, performs semantic vectorization conversion, and calculates and generates quantitative parameters for micro-semantic interaction.

[0027] The core computing and modeling layer module receives quantized parameters to construct a dynamic hypergraph manifold model representing the overall collaborative structure and calculates the macroscopic cognitive entropy of the manifold. Based on the dynamic hypergraph manifold model, macroscopic cognitive entropy, and historical states, the core computing and modeling layer module calculates the thermodynamic free energy and its variation of the system to comprehensively predict the risk of collaborative deadlock.

[0028] The decision and intervention layer module is activated when the risk of collaborative deadlock exceeds a preset threshold. It performs topological feature analysis on the dynamic hypergraph manifold model to locate invalid coordination structures and generate corresponding coordination intervention instructions.

[0029] The decision-making and intervention layer module sends coordination and intervention instructions to an external workflow engine for execution through the support and interface module, thereby reshaping the coordination path between agents and tracking the effects of intervention to form a closed loop.

[0030] The present invention has the following beneficial effects:

[0031] This invention transforms heterogeneous knowledge sources such as documents, databases, APIs, and expert rules into standardized vector and structured knowledge through a unified data access and parsing layer. This knowledge is then stored in a unified knowledge base, achieving "normalized" management of knowledge. It provides a visual workflow designer, allowing users to drag and drop to arrange a complete logical chain from intent recognition, knowledge retrieval, prompting engineering to model reasoning and quality review. It assembles complex business rules and AI capabilities with low-code and embeds a full-process control mechanism that runs through development, testing, and operation and maintenance. It verifies output quality through automated testing and ensures stable and compliant operation in the production environment through a real-time monitoring and alarm system. This achieves seamless integrated management of knowledge fusion, logical arrangement, and quality control.

[0032] Furthermore, it transforms natural language dialogue into computable "information potential energy" and "cognitive state," thereby turning fuzzy "soft coordination" into quantifiable objects. It constructs a dynamic hypergraph model to characterize the global collaborative structure and calculates "cognitive entropy" to measure system disorder in real time. By introducing the "free energy" function and its changing trend, analogous to thermodynamics, the system can predict deadlock risks several steps in advance. When the risk exceeds the threshold, the algorithm does not rely solely on external circuit breaker rules but initiates "topology surgery" to automatically identify and cut off inefficient dialogue connections that lead to loops. At the same time, it establishes efficient guided communication shortcuts for cognitively consistent agents. This is equivalent to implanting "architectural-level hard constraints" based on mathematical computability into the system. Without sacrificing flexibility, it avoids infinite loops in principle and achieves fine-grained, predictive control over costs and processes. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a structural block diagram of the present invention.

[0035] 1. Project initialization module; 2. Knowledge base configuration module; 3. Model service module; 4. Workflow orchestration module; 5. Testing and verification module; 6. Deployment and monitoring module; 7. Data acquisition and injection layer module; 8. Core computing and modeling layer module; 9. Decision and intervention layer module; 10. Support and interface module. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0037] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0038] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0039] In the description of this invention, it should be understood that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0040] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0041] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0042] An AI agent construction system based on vector knowledge base and large model workflow, such as Figure 1 As shown, it includes project initialization module 1, knowledge base configuration module 2, model service module 3, workflow orchestration module 4, testing and verification module 5, and deployment monitoring module 6;

[0043] The output of the project initialization module 1 is connected to the knowledge base configuration module 2 and the model service module 3. It is used to create intelligent agent projects and set the basic information of intelligent agents. The project initialization module 1 includes a web management console, a project configurator and a metadata store. The web management console is connected to the project configurator to receive the intelligent agent project configuration information input by the user and transmit it to the project configurator. The project configurator is connected to the metadata store to encapsulate the received configuration information into structured metadata and store it in the metadata store for other modules in the system to call.

[0044] The output of the knowledge base module is connected to the workflow orchestration module 4, which is used to access multi-source data for vectorization processing and configure the prompt word library and rule library. The knowledge base configuration module 2 includes a multi-source data accessor, a document loader, an embedding model, a vector database, a prompt word library manager, and a rule library manager. The multi-source data accessor is connected to the document loader to transmit the accessed multi-source data to the document loader for parsing. The document loader is connected to the embedding model to transmit the parsed text to the embedding model for vectorization processing. The embedding model is connected to the vector database to store the generated vector data in the vector database and build an index. The prompt word library manager and the rule library manager are set up in parallel to provide the assembled prompt words and the loaded business compliance rules, respectively.

[0045] The output of Model Service Module 3 is connected to Workflow Orchestration Module 4, which is used to configure the core inference service stack for domain-specific fine-tuning. Model Service Module 3 includes an inference service configurator, a vLLM inference engine, a gRPC interface service, and a LoRA fine-tuning pipeline. The inference service configurator connects to and controls the vLLM inference engine to configure high-performance inference parameters and start the service. The output of the inference service configurator is connected to the gRPC interface service, which is connected to the inference service configurator to receive model service instances forwarded by the inference service configurator and provide high-concurrency, low-latency inference APIs to the outside world. The input of the LoRA fine-tuning pipeline is used to access the domain dataset. The output of the LoRA fine-tuning pipeline is connected to the inference service configurator. The LoRA fine-tuning pipeline is used to perform domain-specific fine-tuning on the basic model and provide the generated model version and parameters after fine-tuning to the inference service configurator for loading and updating.

[0046] The output of workflow orchestration module 4 is connected to test and verification module 5, which is used to design workflows for resource orchestration and deployment. Workflow orchestration module 4 includes a visual workflow designer, a workflow engine, a Kubernetes orchestrator, and a task executor. The visual workflow designer is connected to the workflow engine to provide a graphical interface for orchestrating intelligent agent workflows containing multiple logical steps and sending the orchestrated workflow definition to the workflow engine. The workflow engine is connected to the visual workflow designer, Kubernetes orchestrator, and task executor to parse the received workflow definition and convert it into a task sequence organized by a directed acyclic graph and drive the task executor to execute each task in sequence. The Kubernetes orchestrator is connected to the workflow engine to receive resource requests scheduled by the workflow engine and dynamically allocate and schedule GPU nodes and CPU nodes. GPU nodes are dedicated to executing model inference tasks, and CPU nodes are dedicated to executing data retrieval and processing tasks and realizing automatic scaling of resources and secure communication with the service mesh. The task executor is connected to the workflow engine and external services to specifically execute various types of tasks issued by the workflow engine. These tasks include input parsing, knowledge retrieval, prompt word assembly, model invocation, quality review, and response output.

[0047] The output of the test verification module 5 is connected to the deployment monitoring module 6, which is used to test the submitted orchestrated agent workflow. The test verification module 5 includes an end-to-end test engine, a quality evaluator, and a test report generator. The end-to-end test engine is connected to the workflow orchestration module 4 to receive the agent workflow definition under test and simulate real user requests to initiate end-to-end functional tests. The output of the end-to-end test engine is connected to the quality evaluator to send the raw output and process data generated during the test to the quality evaluator. The quality evaluator is connected to the end-to-end test engine and the test report generator to receive test data and perform professional quality assessments, including accuracy assessment, timeliness verification, compliance checks, and response speed tests. The output of the quality evaluator is connected to the test report generator to send the quantitative assessment results and problem details to the test report generator. The test report generator is connected to the quality evaluator and the downstream deployment monitoring module 6 to receive the assessment results, generate a structured test report containing a comprehensive quality score, detailed test item results, and improvement suggestions, and output the verified agent workflow and its quality endorsement to the deployment monitoring module 6 for production deployment.

[0048] The deployment monitoring module 6 is used to deploy the production environment and configure the monitoring and alarm system. The deployment monitoring module 6 includes a deployment manager, a monitoring data collector, an alarm engine, an operations and maintenance console, and a production cluster. The deployment manager connects to the test and verification module 5 and the production cluster. The deployment manager receives verified agent workflows and deploys them to the specified production cluster environment according to preset resource quotas, API gateway, and load balancing configuration policies. The monitoring data collector connects to agent service instances in the production cluster to continuously collect multi-dimensional runtime data, including model call latency, retrieval time, GPU utilization, and business metrics. The output of the monitoring data collector connects to the alarm engine, which connects to the monitoring data collector and the operations and maintenance console to receive the collected monitoring data, perform real-time analysis according to predefined threshold rules, and generate and send alarm information when anomalies or performance bottlenecks are detected. The operations and maintenance console connects to the alarm engine. The operations and maintenance console provides a unified graphical interface for operations and maintenance personnel to view system health status, manage deployment tasks, configure alarm rules, and perform manual intervention.

[0049] The AI ​​agent construction system, in its specific operation, executes a construction method including the following steps:

[0050] The steps for initializing an agent project and building a knowledge system are as follows: First, create an agent project using the project initialization module 1 and set basic agent information. Second, access multi-source data and perform vectorization processing using the knowledge base configuration module 2, configuring the prompt word library and rule base. More specifically, create an agent project in the Web management console, setting the agent name, scope of responsibility, interaction style, and target user group. Third, access multi-source data through the knowledge base management module, including file data sources, database sources, API data sources, and vector knowledge sources. Fourth, parse files using a document loader, convert text into vector representation using an embedding model, store the vector data in a vector database, and create an index. Fifth, configure the prompt word library, select a standard prompt word template, and inject domain-specific analysis prompts. Sixth, configure the rule base. The process loads business logic and compliance rules, sets quality control requirements, and the semantic retrieval quality assessment process calculates the average similarity between test queries and corresponding retrieval results to obtain the semantic retrieval average similarity score. The similarity is calculated using cosine similarity, and the total number of test queries is the evaluation sample size. The relevant parameters are derived from the results generated in the vector retrieval test phase of the knowledge base configuration module 2. In the project configuration completeness assessment, the completeness is the quotient of the number of configured parameters set and the total number of required configuration parameters. The relevant parameters are derived from the project configurator's completeness check results on the user input configuration. The business rule coverage rate is the quotient of the number of rules matched in the current scenario and the total number of rules required in the current scenario. The relevant parameters are derived from the statistical data generated by the rule base manager during the rule matching process.

[0051] Model service configuration and domain optimization steps: Configure the core inference service stack through model service module 3 and perform domain-specific fine-tuning. More specifically, configure a high-performance inference stack in the model service management interface, enable the vLLM inference engine, apply pagination attention optimization and dynamic batch processing, and provide inference services through the gRPC interface. If it is necessary to improve the professionalism, start the LoRA fine-tuning pipeline, upload the domain dataset, set evaluation metrics, and automatically complete training, evaluation and version management. The evaluation value in the comprehensive evaluation of domain adaptability is the weighted sum of the model's accuracy on the domain test set, the completeness evaluation value of the output content, and the security compliance score. The sum of the weight coefficients of each evaluation item is one. The relevant parameters come from the evaluation metrics generated during the training and evaluation phase of the LoRA fine-tuning pipeline. In the calculation of average inference latency, the latency is the quotient of the sum of the response times of all inference requests and the total number of inference requests. The relevant parameters come from the performance metrics recorded by the gRPC interface service during the inference process.

[0052] The steps for intelligent agent workflow orchestration and business logic design are as follows: Intelligent agent workflows are designed using Workflow Orchestration Module 4, and resources are orchestrated and deployed. More specifically, intelligent agent logic is orchestrated in the visual workflow designer, including input parsing and intent recognition, intelligent retrieval and knowledge fusion, prompt word assembly, core reasoning and analysis, quality assessment and compliance review, and response formatting and output. The system operates collaboratively through the workflow engine and Kubernetes orchestrator, scheduling tasks according to DAG dependencies. GPU nodes perform inference, CPU nodes are responsible for data retrieval, resources are automatically scaled, and a service mesh is established to achieve secure communication between microservices. In the workflow execution performance evaluation, the total workflow execution time is the sum of the execution time of all task nodes and the communication overhead of all communication dependencies. The total number of task nodes is the number of independent tasks in the execution process, and the number of communication dependencies is the number of paths for data transmission between tasks. These parameters are derived from performance monitoring data during the workflow engine execution process. The average resource utilization rate is the average ratio of the usage time of various resources during the monitoring period to the total monitoring period time. The number of resource types is the average total number of resource types participating in the calculation. These parameters are derived from resource usage monitoring data collected by the Kubernetes orchestrator.

[0053] The steps for intelligent agent integration testing and quality verification are as follows: The intelligent agent undergoes end-to-end functional testing and professional quality assessment through the testing and verification module 5. Specifically, end-to-end functional testing verifies the accuracy of knowledge retrieval, the logic of prompt word assembly, the quality of model inference output, and the compliance of response format. Professional quality assessment includes accuracy evaluation, timeliness verification, compliance checks, and user experience and response speed testing. The intelligent agent's comprehensive quality index is calculated based on four types of error rates: accuracy error, timeliness error, compliance error, and performance error. The calculation method involves subtracting the product of each error rate and its corresponding weight coefficient from one, followed by a series of products. The weight coefficient reflects the importance of each type of error, and the relevant parameters are derived from error statistics collected during the end-to-end testing and quality assessment phases. The overall test F-score is calculated as twice the product of precision and recall, divided by the sum of precision and recall. Precision and recall are derived from the accuracy assessment data of the test results by the quality evaluator.

[0054] Production Deployment and Operation Monitoring Steps: Deploy the production environment through the deployment monitoring module 6 and configure the monitoring and alarm system. More specifically, select the production cluster and resource quota, configure the API gateway and load balancer, and set automatic scaling rules. Deploy the monitoring and alarm system to cover technical and business indicators. The comprehensive system health index is calculated by dividing the sum of the standardized values ​​of all monitoring indicators within the specified monitoring time window by the product of the monitoring time window and the total number of monitoring indicators. The standardized value of each monitoring indicator is between zero and one. The relevant parameters come from the real-time monitoring data stream collected by the deployment monitoring module 6. The alarm accuracy is calculated as the quotient of the number of alarms actually triggered and the number of problems actually occurring. The relevant parameters come from the comparison data of alarms triggered by the alarm engine and subsequent problem verification. The overall system construction quality assessment is obtained by calculating the weighted sum of the completion assessment scores of each step and the corresponding weight coefficients. The sum of the weight coefficients is one. The completion assessment scores of each step come from the quality assessment data generated during the execution of that step.

[0055] To address the challenge that traditional or existing AI systems often fail to efficiently integrate and assemble diverse domain knowledge assets scattered throughout an enterprise—including internal documents stored in various files, records in structured databases, real-time data streams provided through application programming interfaces, and implicit experience and business rules embedded in the minds of experts—the AI ​​intelligent agent construction system aims to address this issue. This system utilizes a unified, standardized, and easy-to-use low-code platform to rapidly and efficiently assemble and integrate these heterogeneous and largely unstructured knowledge sources, thereby constructing intelligent agents capable of performing complex tasks. The lack of this capability directly leads to a fragmented, cumbersome, and unreliable management dilemma throughout the entire lifecycle of intelligent agent workflow design, task scheduling, and output quality control.

[0056] For unified access and atomic processing of multi-source heterogeneous knowledge, its knowledge base configuration module 2 has a built-in multi-source data accessor, which can connect to common internal document storage systems, relational databases, API services that provide RESTful or GraphQL interfaces, and existing external vector knowledge bases in a configurable manner, thereby breaking down data silos and realizing the convergence of cross-source data.

[0057] For unstructured documents of different formats, such as PDF, Word, Excel and presentations, the system uses an integrated document loader for deep parsing. It can not only extract plain text content, but also recognize and process semi-structured information such as tables, lists and chapter titles, laying the foundation for subsequent semantic understanding.

[0058] Subsequently, the system calls the pre-trained Embedding model to transform the parsed text fragments into dense vector representations in a high-dimensional space. This vectorization process essentially maps knowledge described by human natural language into mathematical objects that can be efficiently computed and compared by machines, which is the cornerstone of achieving semantic-level intelligent retrieval.

[0059] The generated vector data is persistently stored in a dedicated vector database such as Milvus or PGVector, and an efficient near nearest neighbor index is built on top of it. This enables the system to perform semantic similarity-based retrieval, which is far superior to traditional keyword matching. It can more accurately locate relevant knowledge fragments from massive amounts of text, effectively solving the problem of retrieving accurate knowledge from the ocean of unstructured information.

[0060] Meanwhile, the system uses independent prompt word manager and rule base manager to handle expert experience and business process rules. The prompt word manager allows users to create, manage and reuse prompt instructions for different task scenarios in a templated manner. These templates can be dynamically injected with specific query contexts, retrieved knowledge fragments and domain-specific instructions, thus forming a structured guide to guide the large model to generate high-quality and professional responses.

[0061] The rule base manager provides tools to explicitly define tacit knowledge such as complex business logic, compliance requirements, and decision paths. It supports encoding and storing rules in an interpretable form, such as defining under what conditions specific legal provisions must be cited, or setting mandatory specifications for output formats, thereby embedding clear business constraints and decision boundaries into the agent's reasoning process.

[0062] Thus, the system has completed the process of transforming scattered, original, and heterogeneous knowledge resources into a unified, standardized, computable, searchable, and programmable digital knowledge base, providing rich, reliable, and easily accessible "knowledge fuel" for building intelligent agents.

[0063] With a robust knowledge base, the system provides professional and high-performance reasoning capabilities through Model Service Module 3.

[0064] The inference service configurator in this module allows technical administrators to flexibly deploy and optimize the inference service backend for large language models. For example, it can integrate high-performance inference engines such as vLLM, utilize their pagination attention mechanism and dynamic batch processing technology to significantly improve the throughput of the model service and reduce latency, and provide stable and reliable model call services to the outside world through an efficient gRPC communication interface.

[0065] More importantly, to overcome the shortcomings of general-purpose large models in terms of knowledge depth and professionalism in specific vertical domains, the system integrates the LoRA fine-tuning pipeline, a low-threshold domain adaptation tool. Users only need to prepare and upload labeled domain-specific datasets, and the system can automatically complete model training, validation, evaluation, and version management based on efficient parameter fine-tuning methods, producing a "domain expert model" that deeply fits industry terminology, business processes, and professional paradigms. This step enhances the accuracy and credibility of the agent's answers in professional scenarios and is the engine for producing high-quality content.

[0066] The next step is to address the problem of how to flexibly assemble standardized knowledge retrieval capabilities, domain expert models, and embedded business rules into a workflow that can automatically execute complex tasks according to actual business needs. This is the problem that the workflow orchestration module 4 of this system solves.

[0067] This module is a visual, low-code workflow designer. Simply drag and drop to create a complete logical flowchart of an agent's task processing on the canvas. The nodes in the diagram represent different processing stages, such as nodes that receive user input and parse intent, nodes that concurrently retrieve relevant knowledge from vector databases and knowledge graphs, nodes that dynamically assemble prompt words containing context and rules, core nodes that call domain fine-tuning models for deep analysis and reasoning, nodes that automatically review the initial output of the model for quality and compliance, and nodes that format the final result into a specified output structure.

[0068] This visualized logic diagram, typically a directed acyclic graph, is converted into a standard workflow definition by the workflow designer and then parsed and executed by the underlying workflow engine.

[0069] The workflow engine works in deep collaboration with the Kubernetes container orchestrator to instantiate each task node in the logical graph and schedule it to the most suitable computing resources for execution. For example, it automatically schedules computationally intensive model inference tasks to nodes equipped with GPUs and data-intensive retrieval and processing tasks to CPU nodes. At the same time, it dynamically scales up and down the required computing resources according to the overall load and ensures the security, reliability and observability of communication between various microservices through service mesh technology.

[0070] However, the ability to assemble and run workflows does not necessarily mean that the output results are reliable. Therefore, the system has a robust quality assurance system built in, namely the test and verification module 5.

[0071] This module acts as a "quality gatekeeper" before the agent is deployed online. Its end-to-end testing engine can automatically simulate a large number of real user requests, drive the workflow of the agent under test, and record the input, output and intermediate state of each link throughout the process.

[0072] Subsequently, the quality evaluator will perform multi-dimensional and automated quantitative analysis on these results. The evaluation dimensions include the accuracy of answers based on standard answers or expert judgments, the timeliness of checking the timeliness of the cited knowledge sources, the compliance of comparing each rule against the rule base, and the performance of measuring the complete time from request to response.

[0073] These evaluation results are ultimately integrated by the test report generator into a comprehensive quality assessment report, which clearly points out the advantages and disadvantages of the agent workflow under different test cases, provides developers with clear optimization directions, and ensures that only agents that meet the preset quality standards and have stable and reliable performance can be approved to enter the production environment, thereby controlling the quality risks of the final deliverables in advance at the process level.

[0074] Once the intelligent agent passes rigorous testing, the system's deployment and monitoring module 6 is responsible for delivering it smoothly and reliably to the production environment and performing full lifecycle operation and maintenance management.

[0075] The deployment manager receives the "production-ready" intelligent agent workflow package from the test and verification module 5. Based on the predefined deployment strategy, it automatically completes the resource application, service containerization deployment, API gateway configuration, load balancing settings, and application of elastic scaling rules in the production cluster, realizing one-click and standardized production release.

[0076] After the intelligent agent goes online, the monitoring data collector begins to continuously collect comprehensive runtime metrics. These metrics cover both technical infrastructure levels, such as model inference latency, vector retrieval time, GPU memory utilization, and service call error rate, and business application levels, such as task processing throughput and user satisfaction feedback metrics.

[0077] The alarm engine analyzes these continuous monitoring data streams in real time. Once any core indicator deviates from the preset health threshold range, such as an abnormal spike in response latency or a sudden increase in error rate, it will immediately trigger multi-level, multi-channel alarm notifications to promptly remind maintenance personnel to intervene.

[0078] The unified operations and maintenance console provides the operations team with a panoramic visual monitoring dashboard, which can not only grasp the overall health status of all online agents, but also drill down to conduct in-depth investigation and performance analysis of the running details of individual agents, individual workflow instances, and even individual processing nodes, thereby realizing the transformation of the operations and maintenance model from passive response to proactive prevention.

[0079] In summary, this system achieves unified vectorization and structured governance of heterogeneous unstructured knowledge through the knowledge base configuration module 2, enables flexible assembly and automated scheduling of complex business logic in a low-code, visual manner through the workflow orchestration module 4, establishes strict pre-emptive quality quantification assessment and entry thresholds through the testing and verification module 5, and finally ensures stable deployment, real-time insight, and continuous optimization of the production environment through the deployment and monitoring module 6.

[0080] It not only solves the efficiency problem of not being able to assemble quickly, but also significantly reduces the technical threshold and time cost of building AI applications through a low-code platform. Furthermore, through its built-in end-to-end quality control and operation and maintenance guarantee mechanism, it efficiently and reliably transforms its valuable knowledge assets into digital employees with real business value.

[0081] However, when this system is in operation, in the case of handling complex tasks that require multi-step scheduling and collaboration between internal and external systems, multi-agent systems based on natural language communication may fall into a "dialogue dead loop," causing the task to fail to be completed while continuously consuming a large amount of computing power, resulting in huge API costs and resource waste. When this problem occurs, because the agents rely on inefficient and uncertain natural language for negotiation, they are very likely to fall into an invalid loop. Secondly, the workflow design often has vague definitions of abnormal branches and task termination conditions, and lacks a hard circuit breaker mechanism based on quantitative indicators. Finally, the system generally lacks real-time, fine-grained cost and process monitoring for such complex distributed execution processes.

[0082] The combination of dialogue-based soft coordination and the lack of architectural-level hard constraints on infinite loops means that the system sacrifices certainty while pursuing flexibility. Furthermore, more complex prompts or static rules alone cannot guarantee the reliable completion of coordination in principle, which touches on the deep-seated challenges in the reliability design of distributed AI systems.

[0083] Therefore, this construction system is as follows Figure 1 As shown, it also includes a data acquisition and injection layer module 7, a core computing and modeling layer module 8, a decision-making and intervention layer module 9, and a support and interface module 10;

[0084] The data acquisition and injection layer module 7 collects raw dialogue data and task target data between agents, performs semantic vectorization transformation, and calculates and generates quantization parameters for micro-semantic interaction. More specifically, the data acquisition and injection layer module 7 includes a dialogue log collector, a task target manager, a semantic embedding service, and a micro-semantic quantizer connected in sequence. The output of the dialogue log collector is connected to the first input of the semantic embedding service to provide the raw dialogue text; the output of the task target manager is connected to the second input of the semantic embedding service to provide the task description text; the output of the semantic embedding service is connected to the input of the micro-semantic quantizer to provide the semantic vectorization result; the micro-semantic quantizer is configured to perform calculations on the semantic vectorization result and output the quantization parameters for micro-semantic interaction.

[0085] The core computation and modeling layer module 8 receives quantized parameters to construct a dynamic hypergraph manifold model representing the overall collaborative structure and calculates the macroscopic cognitive entropy of the manifold. Based on the dynamic hypergraph manifold model, macroscopic cognitive entropy, and historical states, the core computation and modeling layer module 8 calculates the thermodynamic free energy and its variation of the system to comprehensively predict the risk of collaborative deadlock. More specifically, the core computation and modeling layer module 8 includes a dynamic hypergraph builder, a cognitive entropy calculator, and a system thermodynamic calculator that are sequentially connected and whose data is passed sequentially. The input of the dynamic hypergraph builder receives microscopic semantic interaction quantized parameters from the data acquisition and injection layer module 7 and is configured to construct the dynamic hypergraph manifold model. The input of the cognitive entropy calculator is connected to the output of the dynamic hypergraph builder, receives the dynamic hypergraph manifold model and its weight data, and is configured to calculate the macroscopic cognitive entropy. The input of the system thermodynamic calculator is connected to the output of the cognitive entropy calculator to obtain the macroscopic cognitive entropy and to the data acquisition and injection layer module 7 to obtain historical state data. It is configured to calculate the thermodynamic free energy and its variation, thereby comprehensively predicting the risk of collaborative deadlock.

[0086] The decision and intervention layer module 9 is activated when the risk of collaborative deadlock exceeds a preset threshold. It performs topological feature analysis on the dynamic hypergraph manifold model to locate invalid coordination structures and generate corresponding coordination intervention commands. More specifically, the decision and intervention layer module 9 includes a deadlock risk decision-maker, a topological feature analyzer, and a coordination structure interventioner connected in sequence. The input of the deadlock risk decision-maker is connected to the output of the system thermodynamic calculator in the core computation and modeling layer module 8. It receives the collaborative deadlock risk value and compares it with a preset risk threshold to generate a decision command to trigger intervention. The control end of the topological feature analyzer is connected to the output of the deadlock risk decision-maker, and its data input end is connected to the output of the dynamic hypergraph builder in the core computation and modeling layer module 8. The topological feature analyzer is activated only when a decision command to trigger intervention is received, to perform topological feature analysis based on the current dynamic hypergraph manifold model and locate invalid coordination structures. The input of the coordination structure interventioner is connected to the output of the topological feature analyzer, and it generates specific coordination intervention commands based on the location results of invalid coordination structures.

[0087] The decision-making and intervention layer module 9 sends coordination intervention instructions to the external workflow engine for execution through the support and interface module 10 to reshape the coordination path between agents and track the effects of intervention to form a closed loop. More specifically, the support and interface module 10 includes a workflow engine agent and an intervention effect tracker. The input end of the workflow engine agent is connected to the output end of the coordination structure interventioner in the decision-making and intervention layer module 9, which is used to receive coordination intervention instructions and convert them into control commands that can be executed by the external workflow engine or communication middleware. The input end of the intervention effect tracker is connected to the raw data output end of the data acquisition and injection layer module 7 and is connected to the risk calculation link of the core calculation and modeling layer module 8. It is used to recalculate the collaborative deadlock risk value based on the newly acquired data after the workflow engine agent performs the intervention, and generate an intervention effect evaluation report by comparing the risk values ​​before and after the intervention.

[0088] The system works by including the following steps:

[0089] S1. Data Acquisition and Injection Layer Module 7 collects raw dialogue data and task target data between intelligent agents, performs semantic vectorization transformation, and calculates and generates quantitative parameters for micro-semantic interaction;

[0090] S1.1, captures the raw natural language dialogue message stream between intelligent agents in real time through the dialogue log collector, forming a dialogue tuple with timestamps;

[0091] S1.2, Obtain a precise text description of the currently executing subtask through the Task Target Manager;

[0092] S1.3, the message text and task text description in the dialogue tuple are converted into high-dimensional semantic vectors through semantic embedding service;

[0093] S1.4, the following calculations are performed through the micro-semantic quantizer: Based on the semantic vectors of the sequential messages in the dialogue, the difference between the two is calculated to obtain the semantic gradient vector, which is used to characterize the overall direction and magnitude of the semantic content evolution during the dialogue; by calculating the Euclidean distance between this semantic gradient vector and the target semantic gradient vector derived from the task objective description, the degree to which the dialogue evolution deviates from the task objective is measured; combined with the back-and-forth dialogue round information, an information potential energy parameter between zero and one is generated through a nonlinear function mapping, which characterizes the effectiveness of the dialogue in advancing the task objective; for each agent, the semantic gradient vectors of each dialogue are weighted and summed by the weighted average of all the information potential energy parameters that initiate dialogues, and then divided by the weighted sum. The resulting vector is defined as the cognitive state vector of the agent, which is used to characterize the current focus direction and content of the agent's cognition. The Euclidean magnitude of the cognitive state vector is defined as the cognitive field strength parameter of the agent, which is used to characterize the intensity of its cognitive focus;

[0094] S2. Core Computation and Modeling Layer Module 8 receives quantization parameters, constructs a dynamic hypergraph manifold model representing the overall collaborative structure, and calculates the macroscopic cognitive entropy of the manifold;

[0095] S2.1, the information potential energy parameters and cognitive field strength parameters output by the micro-semantic quantizer are received through the dynamic hypergraph builder;

[0096] S2.2, taking the agent as a node, determines whether two nodes should belong to the same logical cooperation unit based on whether the information potential energy parameter between any two nodes exceeds the first set threshold, and connects all nodes belonging to the same logical cooperation unit to form a hyperedge, thereby constructing a dynamically changing hypergraph structure.

[0097] S2.3, assign a weight value to each hyperedge in the hypergraph. The weight value is equal to the average value of the information potential energy parameters between all nodes connected by the hyperedge.

[0098] S2.4 Assign a weight value to each node in the hypergraph. The weight value is directly taken from the cognitive field strength parameter of the agent corresponding to the node.

[0099] S2.5, using the cognitive entropy calculator, first calculate the average weight of all nodes inside each hyperedge. Then, based on the weight values ​​of all hyperedges in the hypergraph structure and the average weight value of the nodes inside each hyperedge, calculate the cognitive Riemann entropy value of the entire hypergraph manifold using a specific entropy calculation formula. This entropy value serves as a macroscopic quantitative indicator that comprehensively measures the overall coordination and disorder of the system. The higher the value, the more chaotic and disordered the system is.

[0100] S3. Core Computing and Modeling Layer Module 8 further calculates the thermodynamic free energy and its variation of the system based on the dynamic hypergraph manifold model, macroscopic cognitive entropy and historical state, and comprehensively predicts the risk of collaborative deadlock.

[0101] S3.1, receive the cognitive Riemann entropy value output by the cognitive entropy calculator, the cognitive state vector and cognitive field strength parameters of each agent output by the microscopic semantic quantizer through the system thermodynamic calculator, and read the historical free energy data of the previous calculation cycle from the cache;

[0102] S3.2, Calculate the internal energy value of the system. The calculation method is as follows: For each agent, first calculate the sum of the absolute values ​​of each dimension of its cognitive state vector, then multiply it by its own cognitive field strength parameter, and finally sum the product results of all agents.

[0103] S3.3, Calculate the cognitive temperature parameter of the system, which is obtained by calculating the statistical variance of the cognitive field strength parameters of all agents;

[0104] S3.4, combining the internal energy value, the cognitive temperature parameter, and the cognitive Riemann entropy value, calculate the system free energy value at the current moment according to the rule of "internal energy value minus the product of the cognitive temperature parameter and the cognitive Riemann entropy value";

[0105] S3.5, calculate the first variation of the system's free energy value relative to the previous moment, i.e., the difference between the current free energy and the free energy at the previous moment; and calculate the second variation of the change of this first variation relative to an earlier moment, i.e., the difference between the current first variation and the first variation at the previous moment.

[0106] S3.6 The collaborative deadlock risk value at the next moment is calculated using a comprehensive decision function. The input variables and their operational logic of this function are as follows: when the first-order variation is negative, its absolute value is taken as the risk contribution; when the second-order variation is also negative, its absolute value is taken as an additional risk contribution; when the cognitive Riemann entropy value exceeds the second set threshold, the excess value is taken as the third risk contribution; the above three contributions are added together to obtain the final risk quantification value.

[0107] S4. When the risk of collaborative deadlock exceeds the preset threshold, the decision and intervention layer module 9 is activated to perform topological feature analysis on the dynamic hypergraph manifold model to locate the invalid coordination structure and generate corresponding coordination intervention instructions.

[0108] S4.1 The deadlock risk decision-maker compares the calculated collaborative deadlock risk value with the preset risk threshold. If the threshold is exceeded, an intervention command is triggered.

[0109] S4.2 When the intervention command is triggered, the topology feature analyzer is invoked to perform algebraic topology analysis on the current hypergraph structure generated by the dynamic hypergraph builder, calculate the homology group invariant that reflects the number of topological cycles, i.e. the first Betti number, and identify all ill-conditioned hyperedge sets that constitute such topological cycles.

[0110] S4.3 For each hyperedge in the set of ill-conditioned hyperedges, find the pair of agents with the lowest information potential energy parameter value among all agent node pairs inside it;

[0111] S4.4, through the coordinating structure interventioner, generates intervention instructions containing the following two types of specific actions: the first type of instruction applies temporary communication isolation to the agent pair with the lowest information potential energy parameter; the second type of instruction calculates the cosine similarity between the cognitive state vectors of all agent pairs that are not currently cooperating directly and efficiently, selects several agent pairs with the highest similarity exceeding the third set threshold, and establishes a new direct communication channel between them with strong guiding prompts pointing to the task goal.

[0112] S5. The decision-making and intervention layer module 9 sends coordination intervention instructions to the external workflow engine for execution through the support and interface module 10, so as to reshape the coordination path between intelligent agents and track the effects of intervention to form a closed loop.

[0113] S5.1 receives two types of intervention instructions generated by the coordination structure interventionist through the workflow engine agent;

[0114] S5.2, converting intervention instructions into specific control commands that can be recognized and executed by the external agent workflow engine or communication middleware. The conversion includes mapping communication isolation instructions into commands that modify message routing rules between specific agents, and mapping instructions that establish new communication channels into commands that inject predefined system prompt words into the dialogue context of a specified agent.

[0115] S5.3 uses an intervention effect tracker to continuously monitor and collect new agent dialogue data generated after the intervention command is executed, and recalculates the risk value of a new round of collaborative deadlock based on this data. By comparing the trend of risk value changes before and after the intervention, a quantitative assessment report on the effectiveness of the intervention measures is generated.

[0116] By employing the aforementioned system and methodological steps to address the problems of dialogue deadlocks and cost overruns caused by the uncertainty of soft coordination in natural language, the elusive dialogue process is transformed into a series of computable, measurable, and interventionizable mathematical objects, thereby embedding architectural-level deterministic constraints while retaining flexibility.

[0117] The data acquisition and injection layer module 7 performs a refined task. This module acts like the system's sensory nerves, continuously listening to and capturing the raw natural language dialogues flowing between all intelligent agents, while obtaining precise descriptions of task objectives.

[0118] Subsequently, the module calls the semantic embedding service to transform this text information into semantic vectors in a high-dimensional space, laying the foundation for mathematical processing.

[0119] The most crucial step is accomplished by the micro-semantic quantizer, which deeply analyzes the semantic evolution of each round of dialogue. It obtains the semantic gradient vector by calculating the difference between the semantic vectors at the beginning and end of the dialogue. This vector accurately depicts the content direction and intensity of the dialogue.

[0120] Meanwhile, this method calculates the deviation between this gradient and the preset task target direction, and combines it with the number of rounds consumed in the dialogue to generate an information potential energy parameter between zero and one through a nonlinear function mapping. This parameter objectively quantifies the effectiveness of a single dialogue in advancing the task, and the higher the value, the more beneficial the dialogue is.

[0121] Furthermore, for each agent, the method aggregates all its external interactions, uses the information potential energy of each dialogue as weight, and performs a weighted average of its semantic gradient vector to obtain the agent's cognitive state vector. Its direction represents the agent's current focus of thought, and its magnitude, i.e., cognitive field strength, represents the degree of concentration of that focus.

[0122] Thus, the fuzzy natural language negotiation is transformed into three clear quantitative parameters: information potential energy, cognitive state, and cognitive field strength. This makes inefficient, cyclical dialogue manifest as low or even continuously decaying information potential energy, as well as divergent or stagnant cognitive states, thereby making the problem observable for the first time.

[0123] Next, the core computing and modeling layer module 8 takes these parameters and begins to perform modeling and diagnosis.

[0124] The module's dynamic hypergraph builder takes the lead. It treats each agent as a node and determines whether they belong to the same logical cooperative unit based on whether the information potential energy between agent pairs exceeds a set threshold. It then connects all agent clusters belonging to the same unit with a hyperedge, thereby constructing a dynamically evolving hypergraph structure.

[0125] This hypergraph differs from ordinary graphs in that it can more naturally represent collaborative groups of multiple agents. The builder assigns a weight to each hyperedge, the value of which is equal to the average information potential energy between all pairs of agents within that hyperedge, to characterize the average efficiency of the collaborative group.

[0126] At the same time, each node is assigned the cognitive field strength of its corresponding agent as a weight to represent the individual's influence.

[0127] Subsequently, the cognitive entropy calculator operates on this hypergraph model. It calculates the average weight of the nodes within each hyperedge and, combined with the weight of the hyperedge itself, obtains the cognitive Riemann entropy value of the entire system through a specific entropy calculation formula.

[0128] This entropy value is a macroscopic comprehensive indicator that penalizes both inefficient collaborative groups and inattentive agents. Its increase directly and quantitatively reflects that the system as a whole is sliding towards chaos and disorder, which is equivalent to installing a sophisticated dashboard for the health of the system.

[0129] However, this method does not stop at static measurement, but further introduces the predictive power of dynamics.

[0130] The system thermodynamic calculator is activated at this moment, using thermodynamic metaphors to model the dynamic evolution of the system.

[0131] It first calculates the internal energy of the system, which is the sum of the products of the cognitive field strength of all agents and their cognitive state activity, representing the total intensity of cognitive activities within the system.

[0132] Next, it calculates the variance of the cognitive field strength of all agents as the cognitive temperature, which measures the uniformity of attention distribution within the system; a larger variance indicates more severe unevenness in attention distribution. Finally, it calculates the system's free energy using the formula: internal energy minus the product of cognitive temperature and cognitive Riemann entropy. This free energy function is the cornerstone of the method's predictive ability; a decrease in its value indicates that the system tends towards stability and order, while an increase indicates instability.

[0133] More importantly, this method continuously tracks the first-order variation (change in free energy) and the second-order variation (acceleration of change) over time. When both are negative and the cognitive entropy remains high, it indicates that the system is accelerating towards a deadlock point. Based on this, the method can synthesize a forward-looking quantification of collaborative deadlock risk, enabling the prediction of crises rather than merely providing post-event warnings.

[0134] When the predicted risk value exceeds the preset safety threshold, the decision-making and intervention layer module 9 is automatically activated, executing the transition from diagnosis to surgery. The deadlock risk decision-maker in this module first makes the decision to trigger intervention.

[0135] Subsequently, the topology feature analyzer performs in-depth algebraic topology analysis on the current dynamic hypergraph, calculates invariants such as the first Betti number reflecting the number of topological cycles, accurately identifies specific ill-conditioned hyperedge sets that cause network congestion and cycles, and locates the agent pairs with the lowest interaction efficiency within these hyperedges.

[0136] This positioning process is not based on simple rules, but on mathematical analysis of the overall topology of the system, directly addressing the root cause of the problem.

[0137] Finally, based on this analysis, the coordinating structural intervention device generates two sets of precise surgical intervention instructions.

[0138] The first set is the disruption command, which aims to immediately and temporarily forcefully isolate identified inefficient or harmful agent dialogue channels, directly cutting off the energy source of ineffective cycles.

[0139] The second set is the bridge-building instruction. This method calculates the direction cosine similarity between the cognitive state vectors of all currently insufficiently connected agent pairs, selects those agent pairs with highly consistent thinking focus and potential for efficient collaboration but weak current connection, and establishes new direct communication channels between them with clear task guidance prompts, providing positive guidance for the system to explore better solution paths.

[0140] These two types of interventions are not arbitrary, but are based on a deep understanding of the system's potential field and topology, aiming to reshape the underlying connectivity of the coordination network.

[0141] Ultimately, the support and interface module 10 ensures that these decisions from the mathematical world can be applied safely and reliably to the operating system of the physical world.

[0142] The workflow engine agent is responsible for receiving intervention instructions and translating them losslessly into native control commands that the underlying agent platform or middleware can understand, such as modifying the filtering rules of the message router or injecting guiding prompts into the context memory of a specific agent.

[0143] At the same time, the intervention effect tracker initiates closed-loop verification. It re-collects new dialogue data generated after the intervention and drives the entire computing chain to reassess the new round of system status and deadlock risk. By rigorously comparing the changes in key indicators before and after the intervention, it generates a quantitative intervention effect evaluation report.

[0144] This report not only confirms the effectiveness of the intervention, but also provides feedback data for the adaptive optimization of parameters throughout the system.

[0145] In summary, this system and method, through a coherent technical chain of semantic quantization, hypergraph manifold modeling, thermodynamic prediction, and topological intervention, transforms the ambiguity of natural language coordination into mathematical precision, and transforms reactive, post-event circuit breaking into proactive, pre-event prediction and structural reshaping. This implants computable hard constraints at the architectural level, resolving the fundamental contradiction in distributed intelligent agent systems where flexibility is difficult to balance with determinism, and achieving controllability, observability, and optimizability of complex task coordination processes.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An AI agent construction system based on a vector knowledge base and a large model workflow, characterized in that, The project initialization module (1), the knowledge base configuration module (2), the model service module (3), the workflow orchestration module (4), the test verification module (5) and the deployment monitoring module (6) are connected in sequence. The output of the project initialization module (1) is connected to the knowledge base configuration module (2) and the model service module (3), which is used to create an agent project and set the agent basic information. The output of the knowledge base configuration module (2) is connected to the workflow orchestration module (4), which is used to access multi-source data for vectorization processing, configure the prompt library and the rule library. The output of the model service module (3) is connected to the workflow orchestration module (4), which is used to configure the core reasoning service stack for domain specialization fine-tuning. The output of the workflow orchestration module (4) is connected to the test verification module (5), which is used to design a workflow for resource orchestration and deployment. The output of the test verification module (5) is connected to the deployment monitoring module (6), which is used to test the submitted agent workflow after orchestration, and the deployment monitoring module (6) is used to deploy the production environment and configure the monitoring and alarm system.

2. The vector-knowledge-base-and-large-model-workflow-based AI agent construction system according to claim 1, wherein, The project initialization module (1) includes a Web management console, a project configurator and a metadata storage, the Web management console is connected to the project configurator for receiving the agent project configuration information input by the user and transmitting it to the project configurator, and the project configurator is connected to the metadata storage for encapsulating the received configuration information into structured metadata and storing it in the metadata storage for calling by other modules in the system.

3. The vector-knowledge-base-and-large-model-workflow-based AI agent construction system of claim 1, wherein, The knowledge base configuration module (2) includes a multi-source data accesser, a document loader, an Embedding model, a vector database, a prompt library manager and a rule library manager, the multi-source data accesser is connected to the document loader for transmitting the accessed multi-source data to the document loader for parsing, the document loader is connected to the Embedding model for transmitting the parsed text to the Embedding model for vectorization processing, the Embedding model is connected to the vector database for storing the generated vector data in the vector database and establishing an index, and the prompt library manager and the rule library manager are arranged in parallel for providing the assembled prompt words and the loaded business compliance rules, respectively.

4. The vector-knowledge-base-and-large-model-workflow-based AI agent construction system of claim 1, wherein, The model service module (3) comprises an inference service configurator, a vLLM inference engine, a gRPC interface service and a LoRA fine-tuning pipeline, the inference service configurator is connected to and controls the vLLM inference engine for configuring high-performance inference parameters and starting the service, the output end of the inference service configurator is connected to the gRPC interface service, the gRPC interface service is connected to the inference service configurator for receiving the model service instance forwarded by the inference service configurator and providing high-concurrency and low-latency inference API to the outside, the input end of the LoRA fine-tuning pipeline is used to access the field data set, the output end of the LoRA fine-tuning pipeline is connected to the inference service configurator, and the LoRA fine-tuning pipeline is used to fine-tune the base model in the field and provide the fine-tuned model version and parameters to the inference service configurator for loading and updating.

5. The vector-knowledge-base-and-large-model-workflow-based AI agent construction system of claim 1, wherein, The workflow orchestration module (4) comprises a visual workflow designer, a workflow engine, a Kubernetes orchestrator and a task executor, the visual workflow designer is connected to the workflow engine for providing a graphical interface to orchestrate an agent workflow comprising multiple logical steps and sending the completed workflow definition to the workflow engine, the workflow engine is connected to the visual workflow designer, the Kubernetes orchestrator and the task executor for parsing the received workflow definition and converting it into a task sequence organized in a directed acyclic graph, and driving the task executor to execute each task in sequence, the Kubernetes orchestrator is connected to the workflow engine for receiving resource requests scheduled by the workflow engine and dynamically allocating and scheduling GPU nodes and CPU nodes, the GPU nodes are dedicated to executing model inference tasks, the CPU nodes are dedicated to executing data retrieval and processing tasks and realizing automatic scaling of resources and secure communication of service mesh, and the task executor is connected to the workflow engine and external services for specifically executing each type of task issued by the workflow engine, each type of task includes input parsing, knowledge retrieval, prompt assembly, model calling, quality review and response output.

6. The vector-knowledge-base-and-large-model-workflow-based AI agent construction system of claim 1, wherein, The test verification module (5) includes an end-to-end test engine, a quality evaluator and a test report generator, the end-to-end test engine is connected to the workflow orchestration module (4) for receiving the agent workflow definition to be tested and simulating a real user request to initiate an end-to-end function test, the output end of the end-to-end test engine is connected to the quality evaluator for sending the original output and process data generated in the test process to the quality evaluator, the quality evaluator is connected to the end-to-end test engine and the test report generator for receiving test data and performing professional quality evaluation including accuracy evaluation, timeliness verification, compliance check and response speed test, the output end of the quality evaluator is connected to the test report generator for sending the quantitative evaluation results and problem details to the test report generator, and the test report generator is connected to the quality evaluator and the downstream deployment monitoring module (6) for receiving the evaluation results, generating a structured test report containing comprehensive quality score, detailed test item results and improvement suggestions, and outputting the verified agent workflow and its quality endorsement to the deployment monitoring module (6) for production deployment.

7. The vector-knowledge-base-and-large-model-workflow-based AI agent construction system of claim 1, wherein, The deployment monitoring module (6) includes a deployment manager, a monitoring data collector, an alarm engine, an operation and maintenance console, and a production cluster, the deployment manager is connected to the test verification module (5) and the production cluster, the deployment manager is used to receive the verified agent workflow and deploy it to the specified production cluster environment according to the preset resource quota, API gateway and load balancing configuration strategy, the monitoring data collector is connected to the agent service instance in the production cluster for continuously collecting multidimensional runtime data including model call delay, retrieval time consumption, GPU utilization and business indicators, the output end of the monitoring data collector is connected to the alarm engine, the alarm engine is connected to the monitoring data collector and the operation and maintenance console for receiving the collected monitoring data, performing real-time analysis according to the predefined threshold rules and generating and sending alarm information when detecting abnormality or performance bottleneck, the operation and maintenance console is connected to the alarm engine, and the operation and maintenance console can visually access the deployment manager and the monitoring data collector to provide a unified graphical interface for operation and maintenance personnel to view system health status, manage deployment tasks, configure alarm rules and perform manual intervention.

8. The vector-knowledge-base-and-large-model-workflow-based AI agent construction system of claim 1, wherein, It also includes a data collection and injection layer module (7), a core computing and modeling layer module (8), a decision and intervention layer module (9), and a support and interface module (10); The data collection and injection layer module (7) collects original dialogue data and task target data between agents and performs semantic vectorization conversion and calculates quantitative parameters of micro semantic interaction; The core computing and modeling layer module (8) receives the quantitative parameters to construct a dynamic hypergraph manifold model representing the overall collaborative structure and calculates the macro cognitive entropy of the manifold, and based on the dynamic hypergraph manifold model, the macro cognitive entropy and the historical state, the core computing and modeling layer module (8) calculates the thermodynamic free energy and its variation of the system to comprehensively predict the risk of collaborative deadlock; The decision and intervention layer module (9) starts and performs topological feature analysis on the dynamic hypergraph manifold model to locate invalid coordination structure and generate corresponding coordination intervention instructions when the synergy deadlock risk exceeds the preset threshold; The decision and intervention layer module (9) sends the coordination intervention instructions to the external workflow engine for execution through the support and interface module (10) to reshape the coordination path between intelligent agents and track the effect after intervention to form a closed loop.

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