Methods, devices, and equipment for building AI intelligent agents through tripartite collaboration

Abstract

This application relates to a method, apparatus, and device for building a three-party collaborative AI agent. The method includes: initializing based on a three-party collaborative access and full-element process control framework; obtaining terminal value targets including quantified achievement standards through a terminal value target input interface; generating a front-end supply parameter adaptation suggestion table based on the terminal value targets through a demand reverse derivation engine and supply adaptability verification; and generating a verified AI agent program package by compiling and simulating the full-element AI agent based on the terminal value targets, front-end supply parameters, and full-element process control configured by the process control party. This application ensures that the AI ​​agent accurately matches the business value targets by constructing a closed-loop system of three-party collaboration, reverse derivation, logic verification, and simulation verification.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a method, apparatus and equipment for constructing a three-party collaborative AI agent. Background Technology

[0002] Currently, artificial intelligence (AI) technology is rapidly integrating with various industries. AI agents, as automated entities that execute specific tasks or processes, have become a crucial element in enabling business innovation through efficient and reliable construction. With increasingly complex application scenarios, agents not only need to complete pre-defined task sequences but also require high environmental adaptability, resource matching, and certainty in value delivery within real-world business environments. However, traditional agent construction methods generally rely on linear task flow orchestration, focusing solely on the sequential connection of functional steps. This makes it difficult to systematically coordinate the complete support system required for their implementation, leading to a disconnect between development and operation. Consequently, the actual effectiveness and stability of agents face severe challenges.

[0003] Currently, the mainstream construction method in the industry mostly adopts a task flow serialization model. This method focuses on step execution, simplifying the construction of intelligent agents into a process configuration process unilaterally led by the developer. This approach has fundamental limitations: First, it lacks a "full-element process" control perspective that covers hardware resources, skill specifications, execution standards, and quality criteria. The non-functional requirements and runtime constraints of intelligent agents are seriously ignored during the development phase. Second, this model does not establish a collaborative participation architecture among front-end suppliers (providing data and services), process controllers (defining standards and resources), and end-user demanders (proposing value objectives). The construction process is in a "black box" state, resulting in intelligent agents that are often severely out of sync with the supply capabilities, control requirements, and value expectations in actual business scenarios, leading to low adaptability.

[0004] Therefore, existing technologies exhibit significant shortcomings in practical applications: In terms of construction logic, they are overly simplistic, rigidly adhering to step-by-step connections, lacking a systematic configuration of the underlying support conditions and top-level execution criteria upon which the intelligent agent relies for operation, thus creating potential problems for later deployment; in terms of collaboration mechanisms, the three parties (supply, control, and demand) cannot effectively confirm access and interact with content during the construction process, causing the intelligent agent to become detached from the real business context and reduced to an isolated technical demonstration; in terms of value realization, there is a lack of a design mechanism that reverse-engineers from clear terminal business value objectives to front-end technical parameters, resulting in a broken link between intelligent agent configuration and business value creation, making it difficult to guarantee the effectiveness of the final output; in terms of element integration, the configuration information of each link is independent and lacks correlation verification, making the intelligent agent highly susceptible to performance bottlenecks or functional failures due to resource configuration conflicts, interface mismatches, or logical contradictions during runtime. These pain points collectively restrict the leap of AI intelligent agents from "operable" to "valuable" and "highly reliable." Summary of the Invention

[0005] Based on this, this application provides a method for constructing a three-party collaborative AI agent, including: Based on the initialization of the tripartite collaborative access and process full-element control framework, the terminal value target, which includes quantitative compliance standards, is obtained through the terminal value target input interface. Based on the terminal value target, a front-end supply parameter adaptation suggestion table is generated through the demand reverse derivation engine and supply adaptability verification. Based on the terminal value target, front-end supply parameters, and process control configuration of all process elements, an AI agent program package that meets the verification standards is generated through the compilation and simulation operation of the full-element intelligent agent.

[0006] Optionally, the initialization of the tripartite collaborative access and full-element process control framework involves obtaining terminal value targets containing quantified compliance standards through the terminal value target input interface, including: Based on the status signals of the three-party collaborative access completion and the preset process full-element control framework, a unique construction task environment containing the full-element control list is obtained through dedicated port listening, identity verification and initialization interface. Based on the generated task environment, the terminal value target, which includes quantifiable achievement standards, is obtained by pushing the value target input interface and preset integrity verification rules to the terminal demand side.

[0007] Optionally, the step of generating a front-end supply parameter matching suggestion table based on the terminal value target, through a demand-backward derivation engine and supply adaptability verification, includes: Based on the quantitative achievement standards in the terminal value target, the front-end supply parameter adaptation suggestion table is generated through the element transformation algorithm built into the demand reverse derivation engine. Based on the generated adaptation suggestion table, the front-end supply parameters that have been confirmed by the front-end supplier and meet the derived threshold are obtained through the front-end supply parameter configuration interface and the supply adaptability verification process.

[0008] Optionally, the step of generating a verified AI agent program package by compiling and simulating the entire process elements configured by the process control party based on the terminal value target, front-end supply parameters, and process control party includes: Based on the terminal value target, the confirmed front-end supply parameters, and the process control party's input of all process elements through the configuration interface, a logically consistent set of all element configurations is obtained through the process element logical consistency verification. Based on the full set of configuration elements, front-end supply parameters, and terminal value targets, an initial AI agent program package is generated through the compilation of full-element intelligent agents. Based on the generated initial AI agent program package and the actual business data provided by the third party, the value achievement verification is realized through simulation operation engine and deviation rate calculation, and the verified AI agent program package and archived full data are output.

[0009] Optionally, based on the quantitative achievement standards in the terminal value target, the step of generating a front-end supply parameter adaptation suggestion table through the element transformation algorithm built into the demand reverse derivation engine includes: Based on the quantitative achievement standards set in the terminal value target, the element transformation algorithm built into the demand reverse derivation engine is invoked through the demand reverse derivation engine. The element conversion algorithm uses the terminal quantitative compliance standard as the target value and calculates the reasonable range of front-end supply parameters required to achieve the target value through the preset inverse mapping relationship between business value and supply parameters. Based on the calculated compatibility requirements of each supply parameter, a structured front-end supply parameter compatibility suggestion table is automatically generated.

[0010] Optionally, the step of obtaining a logically consistent set of configuration elements based on the terminal value target, confirmed front-end supply parameters, and process elements input by the process control party through the configuration interface, and through logical consistency verification of all process elements, includes: The process uses a full-element logical consistency verification function to check the logical consistency between the input hardware support conditions, skill matching standards, standardized execution steps, and zero-deviation execution criteria according to predefined logical rules. When an inspection reveals logical conflicts between elements, the specific conflict point is located and correction suggestions are pushed out until the logical relationships between all elements are consistent, and a complete set of element configurations with consistent internal logic is obtained.

[0011] Optionally, based on the generated initial AI agent program package and the actual business data provided by the third party, the value achievement verification is realized through a simulation engine and deviation rate calculation, and the verified AI agent program package and archived full data are output, including: Based on the initial AI agent program package generated by compilation and the actual business data provided by the third party, the simulation engine is used to simulate the execution of tasks on the agent program package using the actual business data. Capture and analyze the output results of the intelligent agent in the simulation operation, extract the core value index values ​​actually achieved, calculate the deviation rate between the actual values ​​and the target values ​​in the terminal quantitative achievement standard, quantitatively evaluate the performance of the intelligent agent, and compare it with the preset verification achievement judgment threshold. If the deviation rate does not exceed the threshold, the value verification is deemed to have met the standard, and the final verified AI agent program package is output and the full data is archived. When the deviation rate exceeds the threshold, a process return and readjustment mechanism is triggered.

[0012] This application also provides a device for constructing a three-party collaborative AI agent, the device comprising: The initial access module is used to initialize based on the three-party collaborative access and process full-element control framework. It obtains the terminal value target, which includes quantitative compliance standards, through the terminal value target input interface. The derivation and configuration module is used to generate a front-end supply parameter adaptation suggestion table based on the terminal value target, through the demand reverse derivation engine and supply adaptability verification. The intelligent agent generation module is used to generate a verified AI intelligent agent program package by compiling and simulating the intelligent agents of all elements, based on the terminal value target, front-end supply parameters and process control configuration of all process elements.

[0013] Optionally, the derivation and configuration module further includes: The demand reverse derivation module is used to calculate the adaptability requirements of the front-end supply parameters based on the quantitative achievement standards in the terminal value target, and generate a front-end supply parameter adaptability suggestion table. The supply compatibility verification module is used to receive supplier input through the front-end supply parameter configuration interface according to the compatibility suggestion table, and perform compatibility verification between the supply parameters and the derived threshold; if the standard is not met, an early warning is triggered until front-end supply parameters that have been confirmed and meet the threshold are obtained.

[0014] This application also provides an electronic device for implementing any of the three-party collaborative AI agent construction methods described above, including: The processor is used to perform all computation and process control tasks, including three-party collaborative access monitoring and verification, initialization of the process full-element control framework, calling the demand reverse derivation engine to perform element transformation calculation, performing process full-element logical consistency verification, running the full-element intelligent agent compilation module, and calling the simulation operation engine to perform value achievement verification. The memory, coupled to the processor, is used to store processor-executable program instructions, a predefined rule base for the process full-element control framework, element transformation algorithms for the demand reverse derivation engine, full data of historical task archives, and third-party input content and intermediate derivation data for real-time tasks.

[0015] The beneficial effects of this application are as follows: By constructing a three-party collaborative access mechanism and a process-wide control framework, the front-end supply, process control, and terminal demand are incorporated into a unified automated construction process, achieving precise alignment and efficient collaboration of cross-role demands; and by using a demand reverse derivation engine to deduce front-end supply parameters from the terminal's quantified value target as a starting point, and combining it with process-wide logical consistency verification, the intelligent agent configuration achieves precise mapping and logical self-consistency across the entire link from the target to the execution resources; and by using a simulation operation engine to verify the value achievement of the compiled intelligent agent based on actual business data, and establishing a deviation feedback and process backtracking mechanism, the performance pre-verification and full-process quality closed-loop control of the intelligent agent before delivery are achieved, ultimately ensuring that the output AI intelligent agent can accurately match the terminal business value demands. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings required in the description of the embodiments or the prior art are briefly introduced below. Obviously, the accompanying drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a specific embodiment of the method for building a three-party collaborative AI agent according to this application is shown. Figure 2 This is a device block diagram illustrating a three-party collaborative AI agent building apparatus according to a specific embodiment of this application. Detailed Implementation

[0018] Various exemplary embodiments, features, and aspects of this application will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0019] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0020] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0021] Furthermore, to better illustrate this application, numerous specific details are provided in the following detailed embodiments. Those skilled in the art should understand that this application can be implemented without certain specific details. In some instances, methods, means, components, and circuits well-known to those skilled in the art have not been described in detail in order to highlight the main points of this application.

[0022] This application proposes a three-party collaborative AI agent construction method, aiming to solve key problems in traditional AI agent development, such as fragmented collaboration between demanders, suppliers, and process controllers, reliance on experience for configuration, and difficulty in verifying delivery quality. The core lies in constructing an automated AI agent construction system with a full-element process control framework as its skeleton and demand-driven reverse logic as its main thread. First, a three-party collaborative access mechanism based on a dedicated port is designed to ensure that the front-end supplier, process controller, and end-user can initiate tasks in a controlled and orderly collaborative environment. A demand reverse derivation engine is introduced. Through its built-in element transformation algorithm, it can reverse and accurately derive the necessary range of front-end supply parameters to achieve the quantitative value target defined by the end-user, thereby transforming vague business demands into clear technical configuration requirements, fundamentally changing the traditional parameter configuration model dominated by supply-side experience. Furthermore, a complete full-element process list is defined, covering hardware support, skill standards, execution steps, and zero-deviation criteria. A real-time logical consistency verification function is designed to ensure logical self-consistency among all configuration elements, eliminating the risk of implicit conflicts in the execution process. Finally, by integrating all elements for automated compilation and utilizing a simulation engine to verify value achievement based on real business scenarios, a complete technical closed loop of "target definition, reverse derivation, element configuration, logic verification, compilation and generation, and simulation verification" is formed. This ensures that the final delivered AI agent not only strictly conforms to the terminal value objectives but also achieves optimal resource allocation and execution logic. It significantly improves the collaborative efficiency, configuration accuracy, execution reliability, and predictability of results in the agent building process, providing a systematic solution for achieving high-quality, standardized, and reusable automated production of enterprise-level AI agents.

[0023] Example 1 like Figure 1 The diagram shown is a flowchart of a method for building a three-party collaborative AI agent according to an embodiment of this application. This embodiment specifically includes the following: S100 is initialized based on the tripartite collaborative access and process full-element control framework. Through the terminal value target input interface, it obtains the terminal value target containing the quantitative compliance standard.

[0024] Specifically, based on a pre-defined system architecture, the system continuously monitors the access signals from the three parties through specific logical ports and performs automated verification of the initiator's identity. This ensures that the system can only trigger subsequent processes when all three parties' collaborative access conditions are simultaneously met. The access completion signal drives the initialization of the full-element process control framework, which defines standardized management dimensions and lists throughout the entire intelligent agent building cycle. In the task environment generated during this initialization, the system pushes a structured value target input interface to the interaction port identified as the terminal demand party. This interface mandates the complete definition and input of the value delivery type, core value indicators, and their corresponding quantitative achievement standards. The system simultaneously runs pre-defined integrity verification rules, automatically judging the structural integrity and logical initial screening of the input data. Only terminal value targets that pass verification and contain clear quantitative achievement standards are formally accepted by the system and locked as the sole value benchmark and logical starting point for all subsequent derivation and configuration activities. This ensures clear task responsibilities and measurable targets, providing an unalterable initial input for the entire process.

[0025] S200 generates a front-end supply parameter matching suggestion table based on the terminal value target, through the demand reverse derivation engine and supply adaptability verification.

[0026] Specifically, the system uses the terminal value target obtained and locked in step S100, especially the defined quantitative achievement standard, as the core input and driving basis. It calls the built-in demand reverse derivation engine, which encapsulates an element conversion algorithm that transforms business value indicators into technical parameter requirements. This algorithm uses the quantitative achievement standard as the target value and performs reverse calculations based on a pre-set business value model and parameter association rules to derive the theoretical adaptation range or conditions of the key parameters that the front-end supplier needs to provide to achieve the target value, and automatically generates a structured front-end supply parameter adaptation suggestion table. Subsequently, the system pushes this suggestion table to the corresponding configuration interface of the front-end supplier, initiating a supply adaptability verification process. This process allows the supplier to confirm or adjust parameters based on actual capabilities, while the system verifies the input values ​​in real time according to the threshold rules in the derivation model. This interaction and verification process continues until all parameters input by the supplier are determined by the system to meet the derivation threshold requirements, at which point the final confirmed front-end supply parameter set is formed. Through reverse derivation and interactive verification, abstract value is accurately transformed into an executable technical contract.

[0027] S300 generates a verified AI agent program package by compiling and simulating the entire process elements configured by the process control party, based on the terminal value target, front-end supply parameters, and process control party configuration.

[0028] Specifically, the system synchronously acquires the terminal value target determined in step S100, the front-end supply parameters confirmed in step S200, and the detailed configuration of all process elements entered by the process control party through a dedicated configuration interface. These process elements specifically cover four dimensions: hardware support conditions, skill matching standards, standardized execution steps, and zero-deviation execution criteria. First, the system invokes the process element logical consistency verification function. Based on predefined inter-element dependency and conflict rules, it performs a global scan and logical consistency analysis of all the above input configurations. This verification aims to identify and eliminate logical contradictions between different element configurations, ensuring that hardware support capabilities are sufficient to support the execution steps, skill standards match task requirements, and execution steps comply with the zero-deviation criterion, ultimately outputting a logically self-consistent set of all element configurations. Subsequently, the all-element intelligent agent compilation module is activated. Using the logically consistent all-element configuration set, front-end supply parameters, and terminal value target as comprehensive inputs, it performs automated code generation and encapsulation, outputting the initial AI intelligent agent program package. Finally, the simulation engine loads this program package and injects actual business data samples provided by the third party to simulate task execution. The engine captures the execution results, calculates the deviation rate between the actual value of the achieved core value indicators and the target quantification standard, and automatically evaluates them according to preset verification and compliance judgment criteria. Only when the deviation rate meets the criterion requirements will the system finally output the verified and compliant AI agent program package and archive the entire process data; otherwise, a process backtracking mechanism is triggered. Through logic verification, compilation, and simulation verification, a core closed loop is formed to ensure the quality of agent delivery.

[0029] In summary, this application constructs an automated AI agent development system driven by value, involving three-party collaboration, and encompassing all elements of control. First, addressing the pain points of unclear roles and responsibilities and ambiguous business objectives in traditional agent development, this application designs a three-party collaborative access mechanism based on strict logical port monitoring and identity verification, and initializes a standardized, full-element process control framework. Within this controlled environment, mandatory structured data entry and automated integrity verification ensure the acquisition of a clear, measurable, and verifiable quantified value target for the terminal. This fundamentally solves the problems of target ambiguity and vague responsibility, providing an unshakeable value benchmark and collaborative foundation for the entire process, and achieving clarity and manageability of development tasks. Second, to overcome the inherent problem of technical parameter configuration relying on expert experience and being disconnected from business value, this application introduces a reverse-engineering engine for requirements. This engine takes the locked quantified value target as input, performs reverse calculations with the business model through an embedded element transformation algorithm, intelligently derives the theoretical requirements of the front-end supply parameters necessary to achieve the target, and generates structured suggestions. Subsequently, through a supply adaptability verification process, and under real-time threshold monitoring by the system, confirmation and adjustment interactions are conducted with the supplier to ultimately form a set of technical parameters agreed upon by both parties. This mechanism precisely transforms the vague "what is needed" into the clear "what to configure," achieving a data-driven, precise mapping from business value to technical parameters, significantly improving the scientific nature of the configuration and the alignment with the target. Finally, to ensure the reliability and effectiveness of the intelligent agent in complex real-world environments, this application establishes a complete quality closed loop covering logical consistency verification, automated compilation, and simulation verification. The system performs a global logical scan and conflict resolution based on predefined rules for all input process configuration elements, outputting a logically self-consistent configuration set, preventing execution-stage failures from the design source. Based on this, the full-element compilation module automatically generates an executable package, and finally verifies its value achievement through a simulation running engine injected with real business data, using the quantified deviation rate as the delivery criterion, forming a strongly constrained closed loop of "design-generation-verification." This completely changes the passive mode of "coding-testing" separation and post-delivery quality in traditional development, realizing pre-delivery verification and quality assurance of the intelligent agent, and significantly reducing deployment risks and iteration costs.

[0030] As an optional implementation of this application, optionally, in step S100, based on the initialization of the tripartite collaborative access and process full-element control framework, the terminal value target containing quantified compliance standards is obtained through the terminal value target input interface, including: 110, based on the status signals of the three-party collaborative access completion and the preset process full-element control framework, obtains a unique building task environment containing a full-element control list through dedicated port listening, identity verification and initialization interface.

[0031] Specifically, upon system startup, the network service module continuously listens on three predefined logical ports. These ports uniquely correspond to three types of roles: the front-end supplier, the process controller, and the terminal demander. When any party attempts to initiate a connection, the system triggers an identity verification sub-process. This process does not simply verify the authenticity of the identity but verifies the matching of the digital credentials provided by the initiator with its claimed role and confirms whether it currently possesses the necessary permissions to participate in the collaborative task. Only when connection requests from three different source addresses pass the aforementioned identity and permission verifications, and the system detects that the access status signals of all three parties are set to "complete," is the three-party collaborative access condition deemed met. The completion signal is a necessary prerequisite for triggering all subsequent processes, technically ensuring that task initiation must be based on three-party consensus, avoiding the ambiguity of responsibilities that might arise from unilateral initiation.

[0032] Once access is granted, the system immediately invokes the pre-defined process-wide control framework initialization routine. This framework is a structured template that defines the management dimensions involved in the entire lifecycle of an agent. The initialization process essentially instantiates a blank management context based on the current task ID, containing a comprehensive control checklist including hardware resources, skill specifications, execution procedures, and quality criteria. This instantiated context serves as the unique task setup environment for this task. This environment is uniquely identified in both memory and storage, and all subsequent data inputs, configuration operations, and state transitions are anchored to and recorded within this environment, ensuring the isolation, integrity, and traceability of task data.

[0033] 120. Based on the generated task environment, the terminal value target containing the quantitative achievement standard is obtained by pushing the value target input interface and the preset integrity verification rules to the terminal demand side.

[0034] Specifically, after confirming the task environment is ready, the system will proactively push a structured terminal value target input interface to the interaction port corresponding to the authenticated terminal requester. This interface is not a free text box, but is dynamically generated by the system according to the standard paradigm of value definition in the process full-element control framework. It usually includes several key structured fields: value delivery type (e.g., "efficiency improvement", "cost optimization", "risk control"), core value indicator name (e.g., "order processing throughput", "unit cost", "abnormal interception rate"), and most importantly, the quantitative achievement standard. For the quantitative achievement standard, the interface will guide the requester to input a clear target value and comparison relationship (e.g., "greater than or equal to 99.5%", "less than 10 seconds"), and require the selection of measurement unit and statistical caliber.

[0035] To prevent vague or invalid target inputs, the system runs a pre-defined set of integrity verification rules in the background. These rules not only check if required fields are empty, but also perform a preliminary logical screening of the input content. For example, they verify whether the value delivery type and core value indicators fall within a predefined reasonable combination; they review the validity of the numerical format of the "quantitative achievement standard" and whether it matches the indicator type (e.g., whether the target value for ratio-type indicators is between 0 and 1). Input data must pass through all these rule checkpoints sequentially upon submission. If any rule fails verification, the system immediately provides the end-user with the specific error type and correction instructions, and refuses to accept the data.

[0036] Only when the information submitted by the terminal requester passes all integrity checks will the system officially lock and store this structured information in the current task environment, marking it as the confirmed terminal value target. This target then becomes the only and unalterable highest value benchmark for all subsequent derivation, configuration, and verification activities of the entire system.

[0037] As an optional implementation of this application, optionally, in step S200, based on the terminal value target, a front-end supply parameter adaptation suggestion table is generated through a demand reverse derivation engine and supply adaptability verification, including: 210. Based on the quantitative achievement standards in the terminal value target, the front-end supply parameter adaptation suggestion table is generated through the element transformation algorithm built into the demand reverse derivation engine.

[0038] Specifically, the system uses the quantified achievement standards extracted from the terminal value target locked in step 120 as the input and constraints for this calculation. Subsequently, the system invokes the demand reverse derivation engine, an algorithm module that encapsulates a domain-specific knowledge model. Once activated, the engine first loads the element transformation algorithm that matches the current value delivery type and core value indicators. This algorithm has a built-in inverse mapping function from terminal value to front-end supply parameters and an association rule knowledge base.

[0039] The execution logic of the factor conversion algorithm is as follows: The terminal quantification standard is set as the result value (dependent variable) of the objective function, while the front-end supply parameters to be determined (such as the response latency of the data interface, the concurrency capability of service components, and the frequency and accuracy of data supply) are considered as independent variables. The algorithm performs reverse solving or optimization search based on a pre-defined mathematical model that characterizes how business value is affected by these parameters. The calculation goal is not to obtain a single optimal solution, but rather to output a reasonable numerical range or minimum required threshold for each key front-end supply parameter after comprehensively considering practical technical feasibility and business constraints. For example, for the goal of "order processing success rate ≥ 99.9%", the algorithm may derive a series of parameter requirements such as "average database query response time must be ≤ 50 milliseconds" and "message queue backlog depth warning threshold must be ≤ 100".

[0040] After the calculation process is completed, the engine does not output the raw data. Instead, it drives the report generation module to automatically format each derived parameter requirement (including parameter name, description, derived target range / threshold, unit of measurement and importance level) into a well-structured and clearly listed front-end supply parameter adaptation suggestion table. This table not only contains numerical requirements, but also usually includes a brief explanation of the derivation basis, making it a carrier that combines technical instructions and communication documents.

[0041] 220. Based on the generated adaptation suggestion table, obtain the front-end supply parameters that have been confirmed by the front-end supplier and meet the derived threshold through the front-end supply parameter configuration interface and supply adaptability verification process.

[0042] Specifically, after the system generates a front-end supply parameter adaptation suggestion table, it will push it as the core content to the dedicated front-end supply parameter configuration interface corresponding to the front-end supplier. This interface clearly displays the suggested range or threshold for each derived parameter and provides an input box or selector for each parameter, allowing the front-end supplier representative to review, adjust or confirm the suggested values ​​based on their actual technical stack capabilities, resource status and engineering judgment.

[0043] Simultaneously, the system initiates a supply adaptability verification process in the background. This process is a dynamic, accompanying verification mechanism based on the model constraints used by the reverse inference engine in step 210. When the supplier inputs or adjusts any parameter value on the interface, the system will, either in real time or in batches upon submission, re-substitute the parameter set into the reverse inference model for forward verification. The core of the verification is to determine whether the terminal value indicators that the model can predict can still meet the initially set quantitative target standards under the parameter values ​​currently set by the supplier. The system has a preset inference threshold, which is the performance prediction boundary allowed by the model. If the parameter values ​​input by the supplier cause the model prediction results to fall below the target standard or approach the threshold boundary too closely, the system will immediately trigger a verification alarm.

[0044] The verification alarm is not a simple rejection, but rather clearly indicates which parameter(s) fail to meet the requirements and may suggest adjusting the parameters to a range to re-meet the target. The supplier needs to adjust the input based on the alarm information until all parameters are submitted, the system verification passes, and no more alarms are generated. Only then does the system officially record and lock the set of parameter values ​​finally confirmed by the supplier as the front-end supply parameters that have been confirmed by the front-end supplier and meet the derived threshold.

[0045] As an optional implementation of this application, optionally, in step S300, based on the terminal value target, front-end supply parameters, and the process elements configured by the process control party, a verified AI agent program package is generated through compilation and simulation verification of the full-element intelligent agent, including: 310. Based on the terminal value target, the confirmed front-end supply parameters, and the process control party's input of all process elements through the configuration interface, obtain a logically consistent set of all element configurations through the process element logical consistency verification.

[0046] Specifically, the system needs to simultaneously acquire three core inputs: the terminal value target determined in step 120, the front-end supply parameters confirmed in step 220, and the detailed process elements input by the process control party through its dedicated configuration interface. These elements are clearly defined into four categories: hardware support conditions (such as CPU / memory / GPU specifications, network bandwidth), skill matching standards (such as the required AI model type, algorithm library version, and professional personnel skill level), standardized execution steps (such as workflow DAG graph, microservice call chain), and zero-deviation execution criteria (such as compliance clauses, data security specifications, and output accuracy tolerance range).

[0047] Subsequently, the system calls a full-element logical consistency verification function. This function is not a simple syntax check; its core is an inference engine containing a large number of predefined logical rules. These rules describe the constraints that must be met between different element dimensions, such as: "The peak memory consumption required to execute step A must be less than the available memory promised in the hardware support conditions"; "The deep learning framework version specified in the skill matching standard must be compatible with the specific operator called in the execution step"; "The data encryption level specified in the zero-bias execution criterion must be supported by the security module in the hardware support conditions"; "The data input frequency agreed in the front-end supply parameters must be able to be accommodated by the data processing throughput capacity in the execution step".

[0048] The validation engine performs cross-comparisons and rule matching on the input hardware support conditions, skill matching standards, standardized execution steps, and zero-deviation execution criteria. When the engine finds any configuration that violates a predefined rule, it determines it as a logical conflict, immediately locates the specific elements and rule entries involved, generates a detailed report containing a conflict description and correction suggestions, and pushes it to the process control interface. The process control party must adjust the configuration of the relevant elements according to the suggestions and then re-trigger the validation. This "check-locate-feedback-correct" cycle continues until the validation engine has traversed all rules and found no conflicts. At this point, all input elements are considered to constitute a logically consistent set of full-element configurations.

[0049] 320. Based on the full set of configuration elements, front-end supply parameters, and terminal value objectives, the initial AI agent program package is generated through the compilation of the full-element intelligent agent.

[0050] Specifically, the system uses the logically consistent set of all-element configurations produced in step 310 as the overall blueprint. This set has seamlessly integrated the terminal value target of step 120 and the front-end supply parameters confirmed in step 220. These information together constitute a complete and unambiguous formal description of the AI ​​agent to be built.

[0051] Subsequently, the full-element intelligent agent compilation module is activated. This module pre-configured various intelligent agent architecture templates, code component libraries, and connector logic. Its workflow begins with parsing and translating the input configuration set: transforming standardized execution steps into definition files or source code for specific workflow engines; associating the AI ​​model types and algorithms specified in the skill matching criteria with corresponding model service encapsulation code or API call logic; mapping hardware support conditions to resource constraints in the deployment description file; concretizing front-end supply parameters into client configuration and initialization code for external data sources or services; and simultaneously encoding terminal value objectives and zero-deviation execution criteria into internal monitoring metrics, log points, and assertion checking logic within the intelligent agent.

[0052] The compilation process is not a simple string concatenation, but includes sub-processes such as dependency analysis, component selection, interface adaptation, and code optimization. The module ensures that the generated code parts can be correctly referenced and are compatible with each other, and finally packages them into an initial AI agent program package that can be deployed and run independently. This package typically includes an executable program, configuration files, a list of dependency libraries, deployment scripts, and documentation.

[0053] 330. Based on the generated initial AI agent program package and the actual business data provided by the third party, the value achievement verification is realized through simulation operation engine and deviation rate calculation, and the verified AI agent program package and archived full data are output.

[0054] Specifically, the system obtains the initial AI agent program package generated in step 320, and at the same time requires the three parties to provide sample data or data generation rules that can represent the actual business scenario. This actual business data is used to build a high-fidelity simulation environment.

[0055] The simulation engine then loads the agent package and business data. The engine simulates the agent's complete lifecycle in a production environment: injecting input data, driving the agent to execute its internal workflow, calling all internal and external services, and capturing all outputs, logs, and performance metrics during the agent's operation. After a complete simulation run, the engine enters the analysis phase. The primary task of this phase is to accurately extract the actual achieved values ​​from the agent's outputs, corresponding to the core value indicators defined in step 120. For example, if the goal is "order approval accuracy," the engine needs to calculate the percentage of orders correctly approved by the agent during the simulation.

[0056] Next, the system performs a crucial deviation rate calculation. The extracted actual value is compared with the target value of the quantified achievement standard locked in the terminal value target, and then substituted into a preset deviation calculation formula to calculate a specific deviation rate value. This deviation rate is a quantitative measure of the gap between the agent's current performance and the expected target. Subsequently, the system compares this deviation rate with a pre-defined, rigorously defined verification achievement threshold. This threshold is the upper limit of the allowed performance error, for example, "deviation rate ≤ 0.5%".

[0057] Finally, the process direction is determined by comparing the results. If the deviation rate does not exceed the threshold, the system determines that the value verification of the intelligent agent has passed the standard. Subsequently, the system marks the initial program package generated in step 320 as the final qualified version, outputs the verified AI intelligent agent program package, and automatically packages all inputs, configurations, derivation processes, verification records, compilation results, and simulation verification reports of this task from step 110 onwards into full data for archiving, for auditing, traceability, or model iteration.

[0058] If the deviation rate exceeds the threshold, the verification is deemed unsuccessful. The system will not output a program package but will automatically trigger a process return and readjustment mechanism. This mechanism will intelligently locate the most likely cause based on deviation analysis and trace the task status and related warning information back to the interface of the responsible party, driving them to readjust parameters or configurations. Then, the subsequent processes will be re-executed from the corresponding steps until the verification is finally passed.

[0059] Example 2 As an application example of this application, the specific content is as follows: (I) Subjects Involved and Core Relationships This embodiment involves three core entities, all of which establish stable communication with the AI ​​agent's host computer via TCP / IP (a network communication protocol). The computer has three dedicated ports corresponding to the three parties' access: a front-end supply access port (9401), a process control access port (9402), and a terminal demand access port (9403). The core logic is as follows: the value objectives of the terminal demand party drive the front-end supply configuration, the process control party ensures the intermediate execution links through full-element control, and ultimately realizes that the AI ​​agent's output accurately matches the terminal demand and creates business value.

[0060] (II) Core Steps of Computer Execution 1. Third-party access and framework initialization phase The computer initiates the "build process," simultaneously listening on ports 9401, 9402, and 9403. A three-party collaborative access interface pops up, displaying the real-time access status and identity verification progress of the "front-end supplier, process controller, and terminal demander." Once all three parties have completed identity verification and access, the computer automatically closes the access interface and displays a process-wide control initialization interface. This interface generates a unique intelligent agent build task number and displays a comprehensive control list (terminal value objectives, front-end supply parameters, hardware support conditions, skill matching standards, standardized execution steps, and zero-deviation execution criteria).

[0061] 2. Terminal Value Target Definition Stage The computer pushes the terminal value target input interface to port 9403, awaiting input from the terminal requester. The interface, in accordance with full-element control requirements, has three mandatory fields: "Value Delivery Type," "Core Value Indicator," and "Quantitative Achievement Standard." After the terminal requester completes the input (e.g., the value delivery type is "Restaurant Order Optimization Plan," the core value indicator is "Reduction in Order Return Rate," and the quantitative achievement standard is "Reduction in Order Return Rate ≥ 5%)," the computer automatically verifies the completeness of the content. Upon successful verification, the data is stored in the path / data / element / output / [task number] / , and a value target confirmation pop-up appears for the terminal requester to confirm. Confirmation then triggers the front-end supply configuration process.

[0062] 3. Front-end supply parameter configuration and reverse derivation stage The computer invokes a demand reverse engineering engine, using the quantitative compliance standards of the end-user demand side as the target value, and combines this with a preset element conversion algorithm to deduce the range of supply parameters that the front end needs to provide, generating a "Front-end Supply Parameter Adaptation Suggestion Table." This table is then pushed to port 9401 to the front-end supply parameter configuration interface, which simultaneously displays the derived supply suggestion values ​​(e.g., food supply stability ≥ 98%, order data interface response time ≤ 50ms) for the front-end supplier to confirm or adjust. After the front-end supplier completes the input, the computer performs a supply adaptability check. If the supply parameters do not meet the derived threshold, a supply parameter gap warning interface pops up, indicating the gap parameters and suggesting adjustment directions, until the check passes.

[0063] 4. Process-wide integration and configuration stage The computer pushes the full-element configuration interface to port 9402, waiting for the process controller to input control content. The interface displays input items in columns according to the full-element control framework: Hardware support requirements: such as server computing power ≥ 64 cores, data storage capacity ≥ 500 GB, and communication bandwidth ≥ 100 Mbps; Skills matching standards: For example, data analysts need to have industry order analysis qualifications, and operations and maintenance personnel need to master interface debugging skills and have a test accuracy rate of ≥90%; Standardized execution steps: such as the standardized process of order data collection → abnormal order identification → optimization solution generation → terminal push; Zero-deviation execution criteria: such as 100% data transmission accuracy, scheme generation delay ≤ 1 second, and execution error rate ≤ 0.5%.

[0064] The computer verifies the logical consistency of each element in real time. If the standardized execution steps do not match the hardware support conditions, a conflict prompt interface will immediately pop up to locate the conflict point and push correction suggestions until all elements are logically consistent.

[0065] 5. Agent Generation and Value Validation Stage After all three parties' inputs pass verification, the computer activates the "Full-Element Intelligent Agent Compilation Module," integrating front-end supply parameters, full-element process configurations, and terminal value objectives to generate a complete AI intelligent agent program package. Subsequently, it calls the simulation engine, inputting actual business data provided by the three parties for value verification. A value achievement verification interface pops up, displaying in real-time the deviation rate between the intelligent agent's output and the terminal's quantitative achievement standards (e.g., a simulated order cancellation rate reduction of 6.2% results in a deviation rate of 0.2%). If verification is successful, the computer pushes the program package to the path specified by the terminal requester and automatically archives the three parties' inputs, the derivation report, and the value verification results; if verification fails, it returns to the corresponding stage for readjustment.

[0066] Example 3 Based on the same principle as the aforementioned methods, a three-party collaborative AI agent construction device is also proposed, see [link to relevant documentation]. Figure 2 An embodiment of this disclosure provides a three-party collaborative AI agent building device 100, comprising: The initial access module 110 is used to initialize based on the tripartite collaborative access and process full-element control framework, and obtain the terminal value target containing the quantitative compliance standard through the terminal value target input interface; The derivation and configuration module 120 is used to generate a front-end supply parameter adaptation suggestion table based on the terminal value target through the demand reverse derivation engine and supply adaptability verification. The intelligent agent generation module 130 is used to generate a verified AI intelligent agent program package by compiling and simulating the intelligent agent of all elements, based on the terminal value target, front-end supply parameters and process control party configuration of all process elements.

[0067] As an optional implementation of this application, the derivation and configuration module 120 may further include: The demand reverse derivation module 121 is used to calculate the adaptability requirements of the front-end supply parameters and generate a front-end supply parameter adaptability suggestion table based on the quantitative achievement standards in the terminal value target by calling the element transformation algorithm built into the demand reverse derivation engine. The supply compatibility verification module 122 is used to receive supplier input through the front-end supply parameter configuration interface according to the compatibility suggestion table, and perform compatibility verification between the supply parameters and the derived threshold; if the standard is not met, an early warning is triggered until the front-end supply parameters that have been confirmed and meet the threshold are obtained.

[0068] Obviously, those skilled in the art should understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the control methods described above. The modules or steps of this application described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, this application is not limited to any specific hardware and software combination.

[0069] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the control methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0070] Example 4 Furthermore, this application proposes an electronic device characterized by a method for constructing an AI agent to achieve any of the aforementioned three-party collaborative mechanisms, comprising: The processor is used to perform all computation and process control tasks, including three-party collaborative access monitoring and verification, initialization of the process full-element control framework, calling the demand reverse derivation engine to perform element transformation calculation, performing process full-element logical consistency verification, running the full-element intelligent agent compilation module, and calling the simulation operation engine to perform value achievement verification. The memory, coupled to the processor, is used to store processor-executable program instructions, a predefined rule base for the process full-element control framework, element transformation algorithms for the demand reverse derivation engine, full data of historical task archives, and third-party input content and intermediate derivation data for real-time tasks.

[0071] The electronic device of this disclosure includes a processor and a memory for storing processor-executable instructions. The processor is configured to implement any of the aforementioned three-party collaborative AI agent construction methods when executing the executable instructions.

[0072] It should be noted that the number of processors can be one or more. Furthermore, the electronic device in this embodiment may also include input devices and output devices. The processor, memory, input devices, and output devices can be connected via a bus or other means, without specific limitations herein.

[0073] The memory, serving as a computer-readable storage medium for automated fault handling and self-learning methods in modules, can be used to store software programs, computer-executable programs, and various modules, such as the program or module corresponding to the three-party collaborative AI agent construction method in this disclosure. The processor executes various functional applications and data processing of the electronic device by running the software programs or modules stored in the memory.

[0074] Input devices can be used to receive input digital numbers or signals. These signals can be key signals related to user settings and function control of the device / terminal / server. Output devices can include display devices such as screens.

[0075] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for constructing a three-party collaborative AI agent, characterized in that, include: Based on the initialization of the tripartite collaborative access and process full-element control framework, the terminal value target, which includes quantitative compliance standards, is obtained through the terminal value target input interface. Based on the terminal value target, a front-end supply parameter adaptation suggestion table is generated through the demand reverse derivation engine and supply adaptability verification. Based on the terminal value target, front-end supply parameters, and process control configuration of all process elements, an AI agent program package that meets the verification standards is generated through the compilation and simulation operation of the full-element intelligent agent.

2. The method for constructing a three-party collaborative AI agent as described in claim 1, characterized in that, The initialization of the tripartite collaborative access and full-element process control framework involves obtaining terminal value targets containing quantifiable compliance standards through the terminal value target input interface, including: Based on the status signals of the three-party collaborative access completion and the preset process full-element control framework, a unique building task environment containing the full-element control list is obtained through dedicated port listening, identity verification and initialization interface. Based on the generated task environment, the terminal value target, which includes quantifiable achievement standards, is obtained by pushing the value target input interface and preset integrity verification rules to the terminal demand side.

3. The method for constructing a three-party collaborative AI agent as described in claim 1, characterized in that, Based on the terminal value target, a front-end supply parameter adaptation suggestion table is generated through a demand-backward derivation engine and supply adaptability verification, including: Based on the quantitative achievement standards in the terminal value target, the front-end supply parameter adaptation suggestion table is generated through the element transformation algorithm built into the demand reverse derivation engine. Based on the generated adaptation suggestion table, the front-end supply parameters that have been confirmed by the front-end supplier and meet the derived threshold are obtained through the front-end supply parameter configuration interface and the supply adaptability verification process.

4. The method for constructing a three-party collaborative AI agent as described in claim 1, characterized in that, The process, based on the terminal value target, front-end supply parameters, and process control configuration, involves compiling and simulating the entire process to generate a validated AI agent program package, including: Based on the terminal value target, the confirmed front-end supply parameters, and the process control party's input of all process elements through the configuration interface, a logically consistent set of all element configurations is obtained through the process element logical consistency verification. Based on the full set of configuration elements, front-end supply parameters, and terminal value targets, an initial AI agent program package is generated through the compilation of full-element intelligent agents. Based on the generated initial AI agent program package and the actual business data provided by the third party, the value achievement verification is realized through simulation operation engine and deviation rate calculation, and the verified AI agent program package and archived full data are output.

5. The method for constructing a three-party collaborative AI agent as described in claim 3, characterized in that, Based on the quantitative achievement standards in the terminal value target, the front-end supply parameter adaptation suggestion table is generated through the element transformation algorithm built into the demand reverse derivation engine, including: Based on the quantitative achievement standards set in the terminal value target, the element transformation algorithm built into the demand reverse derivation engine is invoked through the demand reverse derivation engine. The element conversion algorithm uses the terminal quantitative compliance standard as the target value and calculates the reasonable range of front-end supply parameters required to achieve the target value through the preset inverse mapping relationship between business value and supply parameters. Based on the calculated compatibility requirements of each supply parameter, a structured front-end supply parameter compatibility suggestion table is automatically generated.

6. The method for constructing a three-party collaborative AI agent as described in claim 4, characterized in that, The process involves verifying the logical consistency of all process elements based on the terminal value target, confirmed front-end supply parameters, and process control parties through the configuration interface. This process yields a logically consistent set of configuration elements, including: The process uses a full-element logical consistency verification function to perform logical consistency checks on the input hardware support conditions, skill matching standards, standardized execution steps, and zero-deviation execution criteria based on predefined logical rules. When an inspection reveals logical conflicts between elements, the specific conflict point is located and a correction suggestion is pushed out until the logical relationships between all elements are consistent, and a complete set of element configurations with consistent internal logic is obtained.

7. The method for constructing a three-party collaborative AI agent as described in claim 4, characterized in that, The process involves using the generated initial AI agent program package and actual business data provided by the third party, along with a simulation engine and deviation rate calculation, to verify the achievement of value targets. The result is the output of a verified AI agent program package and a complete archive of data, including: Based on the initial AI agent program package generated by compilation and the actual business data provided by the third party, the simulation engine is used to simulate the execution of tasks on the agent program package using the actual business data. Capture and analyze the output results of the intelligent agent in the simulation operation, extract the core value index values ​​actually achieved, calculate the deviation rate between the actual values ​​and the target values ​​in the terminal quantitative achievement standard, quantitatively evaluate the performance of the intelligent agent, and compare it with the preset verification achievement judgment threshold. If the deviation rate does not exceed the threshold, the value verification is deemed to have met the standard, and the final verified AI agent program package is output and the full data is archived. When the deviation rate exceeds the threshold, a process return and readjustment mechanism is triggered.

8. A three-party collaborative AI agent construction device, the device comprising: The initial access module is used to initialize based on the three-party collaborative access and process full-element control framework. It obtains the terminal value target, which includes quantitative compliance standards, through the terminal value target input interface. The derivation and configuration module is used to generate a front-end supply parameter adaptation suggestion table based on the terminal value target, through the demand reverse derivation engine and supply adaptability verification. The intelligent agent generation module is used to generate a verified AI intelligent agent program package by compiling and simulating the intelligent agents of all elements, based on the terminal value target, front-end supply parameters and process control party configuration of all process elements.

9. The tripartite collaborative AI agent construction device according to claim 8, wherein the derivation and configuration module further comprises: The demand reverse derivation module is used to calculate the adaptability requirements of the front-end supply parameters based on the quantitative achievement standards in the terminal value target, and generate a front-end supply parameter adaptability suggestion table. The supply compatibility verification module is used to receive supplier input through the front-end supply parameter configuration interface according to the compatibility suggestion table, and perform compatibility verification between the supply parameters and the derived threshold; if the standard is not met, an early warning is triggered until front-end supply parameters that have been confirmed and meet the threshold are obtained.

10. An electronic device, characterized in that, The method for constructing a three-party collaborative AI agent as described in any one of claims 1 to 7 includes: The processor is used to perform all computation and process control tasks, including three-party collaborative access monitoring and verification, initialization of the process full-element control framework, calling the demand reverse derivation engine to perform element transformation calculation, performing process full-element logical consistency verification, running the full-element intelligent agent compilation module, and calling the simulation operation engine to perform value achievement verification. The memory, coupled to the processor, is used to store processor-executable program instructions, a predefined rule base for the process full-element control framework, element transformation algorithms for the demand reverse derivation engine, full data of historical task archives, and third-party input content and intermediate derivation data for real-time tasks.

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