Enterprise ai agent running framework component evaluation and configuration optimization method and system

Abstract

The application discloses an enterprise AI intelligent agent running framework component evaluation and configuration optimization method and system, belongs to the technical field of artificial intelligence and software engineering, and decomposes an intelligent agent running framework into four core components of task planning, information memory, execution feedback and context management; two differentiated implementation schemes are designed for each core component, a full factorial experimental design is constructed, and multiple component configuration schemes are generated; large-scale evaluation is carried out across multiple underlying large language models and multiple task fields, and performance data is collected; the main effect and the interaction effect between components of each core component implementation scheme are calculated through statistical analysis, the implementation scheme dependency of each core component and the field dependency of the implementation scheme are identified; and finally, the optimal component configuration suitable for the target task is screened. The application solves the problem that the prior art can only evaluate the overall performance and cannot quantitatively analyze the independent contribution and dependency of the components, and provides a quantitative basis for scientific configuration and performance optimization of the enterprise AI intelligent agent framework.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and software engineering technology, and in particular to a method and system for evaluating and optimizing the configuration of enterprise AI intelligent agent operating framework components. Background Technology

[0002] With the rapid development of artificial intelligence technology, AI agents are being widely used in areas such as enterprise digital transformation, automated office work, and intelligent decision-making. More and more companies are building industry-specific AI agent systems based on open-source frameworks such as LangChain and AutoGPT.

[0003] The overall performance of an enterprise AI agent depends not only on the fundamental capabilities of the underlying Large Language Model (LLM), but also heavily on the component design and configuration strategies of the upper-layer runtime framework. A typical agent runtime framework includes core functional modules such as task planning, information memory, execution feedback, and context management. The implementation of different modules directly determines the agent's inference efficiency, stability, and accuracy in complex tasks.

[0004] However, existing technologies have significant shortcomings in the evaluation and optimization of intelligent agent frameworks. Current mainstream evaluation methods mostly focus on overall performance comparison, ranking different frameworks or different underlying models only. They cannot break down the independent contribution of each core component within the framework to the final performance, nor can they quantify the mutual influence between components. When a framework performs better overall, existing methods cannot distinguish whether its advantage comes from planning strategies, memory mechanisms, feedback processing, or context organization. This leads to a lack of scientific basis for enterprises when selecting and configuring frameworks, forcing them to rely on experience or blind trial and error.

[0005] Further research revealed that the same functional concept (such as task planning) can have multiple drastically different implementation schemes, and these schemes may produce opposite performance effects under the same conditions. For example, multi-step decomposition planning can significantly improve performance in mathematical reasoning tasks, but it leads to a decrease in accuracy in code generation tasks. This phenomenon demonstrates a clear implementation dependency: the actual effect of a component is determined by its specific implementation method, rather than solely by its functional definition. Furthermore, the same implementation scheme exhibits vastly different, even reversed, effects across different task domains (mathematics, code, retrieval, tool invocation), demonstrating a significant domain dependency: there is no universally applicable optimal implementation scheme across all domains.

[0006] Current technologies lack a systematic and standardized component-level evaluation system, and are deficient in experimental design methods with controllable variables, reproducibility, and quantifiability. They also lack quantitative analysis tools for implementation and domain dependencies. Different research teams employ varying task sets, evaluation metrics, and experimental environments, making it difficult to compare, verify, and reuse evaluation conclusions across different teams. This severely restricts the scientific design, efficient optimization, and large-scale deployment of enterprise AI agent frameworks.

[0007] In summary, there is an urgent need for a method that can modularly disassemble the core components of the framework, conduct comparative experiments on system design, quantitatively analyze the independent and interactive effects of components, and accurately identify the evaluation and optimization of implementation and domain dependencies, so as to provide objective and reliable technical support for the component selection and configuration optimization of the enterprise AI intelligent agent operating framework. Summary of the Invention

[0008] To address the problems in the background technology, this invention provides a method and system for evaluating and optimizing the configuration of enterprise AI intelligent agent operating framework components. Through modular decomposition of framework components, full factorial experimental design, and statistical effect analysis, it achieves a quantitative assessment of the implementation scheme dependence and domain dependence of framework components.

[0009] To achieve the above objectives, this invention provides a method for evaluating and optimizing the configuration of enterprise AI agent operating framework components, including: The intelligent agent operation framework is broken down into four independently controllable core components: task planning component, information memory component, execution feedback component, and context management component. For each core component, at least two implementation schemes with substantial technical differences are designed, with each implementation scheme corresponding to a different technical route and design logic; Based on all implementation schemes of the four core components, a full factorial experimental design matrix is ​​constructed to generate a variety of complete core component configuration schemes. Multiple underlying large language models with different capability levels were selected, and each complete component configuration scheme was independently evaluated in multiple task domains, including mathematical reasoning, code generation, information retrieval, and multi-step tool usage domains. Evaluation data was collected, including accuracy, completion rate, and step efficiency. Statistical analysis was performed on the evaluation data to calculate the main effects of each implementation scheme of each core component, as well as the interaction effects between different core components; Based on the main effect and interaction effect data, identify the implementation scheme dependencies of each core component and the domain dependencies of each implementation scheme; Based on the dependency identification results, the optimal component configuration of the intelligent agent operation framework that is suitable for the enterprise's target tasks is selected, and the framework configuration optimization is completed.

[0010] As a further improvement of the present invention, two implementation schemes are designed for the task planning component: Variant A1 adopts multi-step decomposition planning, which explicitly decomposes the task into an ordered list of subtasks and executes them step by step; Variant B1 adopts mind chain implicit planning, which completes planning and execution in a single reasoning through prompting engineering guidance model.

[0011] As a further improvement of the present invention, two implementation schemes are designed for the information memory component: variant A2 is a complete historical memory that retains all interactive historical data; variant B2 is a summary memory that automatically summarizes and compresses historical data exceeding a preset length for storage.

[0012] As a further improvement of the present invention, two implementation schemes are designed for the execution feedback component: variant A3 is direct feedback transmission, transmitting the tool call result and environmental feedback signal as is; variant B3 is structured feedback parsing, formatting the feedback result and extracting key information before transmission.

[0013] As a further improvement of the present invention, two implementation schemes are designed for the context management component: variant A4 is a fixed template context, which uses a predefined template to organize the context content; variant B4 is a dynamic context trimming, which adjusts the context content in real time according to the task running status.

[0014] As a further improvement of the present invention, the implementation scheme dependency of the core component refers to the characteristic that two implementation schemes of the same core component produce significantly different or opposite performance effects under the same experimental conditions; the domain dependency of the implementation scheme refers to the characteristic that the same implementation scheme of the same core component exhibits opposite performance effects in different task domains.

[0015] As a further improvement of the present invention, the main effect is the independent average impact of the implementation scheme of a single core component on the overall performance of the framework when other core components remain unchanged; the interaction effect is the additional performance impact generated by a specific implementation combination of multiple core components, which exceeds the sum of the main effects of each core component.

[0016] This invention also provides an enterprise AI agent operating framework component evaluation and configuration optimization system, including a modular agent framework, a factorial experiment configuration controller, a multi-domain multi-model evaluation engine, a statistical effect analysis module, a dependency detection and configuration optimization module, and a report generation module connected in sequence. The modular intelligent agent framework has four independently pluggable and replaceable component slots, corresponding to the task planning component, information memory component, execution feedback component and context management component, respectively, allowing for flexible assembly of variants of each core component; The factorial experiment configuration controller is responsible for generating configuration schemes for each complete core component, scheduling and managing the execution flow of each complete core component configuration scheme; The multi-domain, multi-model evaluation engine connects multiple underlying large language models, automatically executes the configuration schemes of each complete core component across models and task domains, and collects performance data; The statistical effect analysis module calculates the main effects of each implementation scheme of each core component and the interaction effects between different core components; The dependency detection and configuration optimization module identifies the implementation scheme dependencies of each core component and the domain dependencies of each implementation scheme, filters the optimal component configuration of the intelligent agent operation framework that is adapted to the enterprise's target tasks, and outputs the optimal component configuration scheme. The report generation module is responsible for generating structured evaluation reports and configuration optimization reports.

[0017] As a further improvement of the present invention, the component slots of the modular intelligent agent framework support the implementation of new component types and implementation schemes through standard interfaces, and automatically update the full factorial experimental design matrix.

[0018] As a further improvement of the present invention, the multi-domain multi-model evaluation engine supports distributed deployment, can execute evaluation tasks with different complete core component configuration schemes in parallel, and supports breakpoint resumption and result aggregation of evaluation tasks.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention, through modular decomposition of the core components of the intelligent agent operating framework, full factorial experimental design, and statistical effect analysis, achieves for the first time quantitative identification and evaluation of the implementation scheme dependence and domain dependence of framework components. It overcomes the shortcomings of existing technologies that can only evaluate overall performance and cannot decompose the independent contributions and interactive effects of components. It provides a scientific, objective, and reproducible quantitative basis for component selection and configuration optimization of enterprise AI intelligent agent frameworks, effectively avoiding the problems of low efficiency, blind configuration, and uncontrollable effects caused by traditional experience-based trial and error mode, and significantly improving the adaptability, stability, and operating performance of enterprise AI intelligent agents in different task domains.

[0020] This invention designs two differentiated implementation schemes for the four core components of task planning, information memory, execution feedback, and context management, accurately covering different technical routes and design logics. This ensures that experimental variables are controllable and comparison dimensions are comprehensive, providing a high-quality and highly discriminative experimental data foundation for subsequent dependency identification. It effectively solves the problems of existing technologies having single component variations, imprecise experimental design, and poor reliability of conclusions.

[0021] This invention clearly defines the calculation logic of the main effect of core components and the interaction effect between components. It can accurately distinguish the independent impact of a single component implementation scheme and the synergistic impact of multiple component combinations. It breaks through the limitation of existing technologies that cannot quantify the interaction between components, and can deeply reveal the root cause of the framework performance bottleneck, providing precise guidance for targeted component replacement and optimization.

[0022] This invention constructs a modular, pluggable intelligent agent evaluation system that supports flexible assembly of component variants, automatic scheduling of experimental configurations, and parallel evaluation of multiple models and multiple domains. It significantly improves evaluation efficiency and automation, reduces the technical threshold and implementation cost for enterprises to conduct component-level evaluation, and adapts to the large-scale, multi-scenario intelligent agent optimization needs of enterprises.

[0023] This invention supports flexible expansion of component types and the number of variants, adapting to the functional iteration and technical upgrade needs of intelligent agent frameworks. The evaluation system has good versatility and portability, and is not only applicable to general enterprise AI intelligent agent frameworks, but can also be quickly adapted to the evaluation and optimization of specialized intelligent agents in vertical fields such as law, medicine, and finance. It has a wide range of applications and strong practicality. Attached Figure Description

[0024] Figure 1 This is a diagram of the modular intelligent agent operation framework and factorial experimental evaluation system architecture disclosed in one embodiment of the present invention; Figure 2 This is a flowchart of the evaluation process for a modular intelligent agent operation framework component disclosed in one embodiment of the present invention. Detailed Implementation

[0025] 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] The present invention will now be described in further detail with reference to the accompanying drawings: like Figure 1 , 2 As shown, the enterprise AI intelligent agent operation framework component evaluation and configuration optimization method provided by the present invention includes: S1. The intelligent agent operation framework is decomposed into four independently controllable core components, namely the task planning component, the information memory component, the execution feedback component, and the context management component; This step involves modularly decomposing core functions to achieve component decoupling and independent control, laying the foundation for subsequent variable controllability and component-level attribution analysis, and avoiding evaluation blind spots caused by overall framework coupling.

[0027] S2, such as Figure 1 As shown, for each core component, at least two implementation schemes with substantial technical differences are designed, with each implementation scheme corresponding to different technical routes and design logic; In some embodiments, for each core component, it is preferable to design two implementation schemes with substantial technical differences; Furthermore, the two implementation schemes of the task planning component are as follows: Variant A1 adopts multi-step decomposition planning, which explicitly decomposes the task into an ordered list of subtasks and executes them step by step. In some embodiments, ReAct-style decomposition planning can be adopted, which explicitly decomposes the task into a cyclical sequence of "think-act-observe" steps; Variant B1 adopts mind chain-style implicit planning, which guides the model to complete planning and execution in a single reasoning through prompting engineering.

[0028] The two implementation schemes of the information memory component are as follows: Variant A2 is a complete history memory, which retains all interaction history data; Variant B2 is a summary memory, which automatically summarizes and compresses historical data that exceeds a preset length, such as setting the preset length to 4000 characters.

[0029] The two implementation schemes for the execution feedback component are: Variant A3 is direct feedback transmission, transmitting tool call results and environmental feedback signals as is; Variant B3 is structured feedback parsing, formatting the feedback results and extracting key information before transmission.

[0030] The two implementation schemes of the context management component are: variant A4 is a fixed template context, which uses a predefined template to organize the context content; variant B4 is a dynamic context trimming, which adjusts the context content in real time according to the task running status.

[0031] This step configures differentiated implementation schemes for each core component, ensuring clear variable dimensions and sufficient comparison dimensions, effectively covering mainstream technical routes, and providing a highly discriminative experimental foundation for subsequent accurate identification of dependencies.

[0032] S3, All implementation schemes based on the four core components, such as Figure 1 As shown, a full factorial experimental design matrix is ​​constructed to generate various complete core component configuration schemes; In some embodiments, such as Figure 2 As shown, by combining the two implementation schemes (variants) of each of the four core components, we get 16 complete core component configuration schemes, which is 2 to the power of 4. Furthermore, a unified configuration management file is used to record the component variant combinations of each complete core component configuration scheme. For example, one complete core component configuration scheme is: the task planning component adopts variant A1, the information memory component adopts variant B2, the execution feedback component adopts variant A3, and the context management component adopts variant B4.

[0033] This step exhausts all component combinations through a full factorial design, avoiding sampling bias and omission of key configurations, ensuring that the experiment covers the complete variable space, and guaranteeing the comprehensiveness and reliability of subsequent effect analyses.

[0034] S4, such as Figure 2 As shown, multiple underlying large language models with different ability levels were selected, and each complete component configuration scheme was independently evaluated in multiple task domains, including mathematical reasoning, code generation, information retrieval, and multi-step tool usage domains. Evaluation data was collected, including accuracy, completion rate, and step efficiency. In some embodiments, the experiment scheduler executes each complete core component configuration scheme sequentially, running on the same underlying language model and the same task set; after each complete core component configuration scheme runs the complete task set, metrics such as accuracy, completion rate, and step efficiency are collected; to ensure statistical reliability, each complete core component configuration scheme is run three times on each task, and the average value of each metric is taken.

[0035] In other embodiments, for multi-model, multi-domain evaluation, three large language models with different ability levels are selected as the underlying engines, representing high ability, medium ability, and low ability levels respectively; the task domains cover mathematical reasoning (e.g., including 100 multi-step math problems), code generation (e.g., including 80 programming tasks), information retrieval (e.g., including 60 multi-hop question-answering tasks), and tool usage (e.g., including 50 multi-step tool calling tasks). A total of 16 complete core component configuration schemes × 3 models × 4 domains × 3 repetitions = 576 sets of evaluation runs were conducted, collecting more than 2,000 valid evaluation results.

[0036] This step involves large-scale evaluation across multiple models, domains, and replicates to ensure a unified experimental environment and sufficient data volume, effectively reducing random errors and significantly improving the stability, universality, and reliability of the evaluation results.

[0037] S5, such as Figure 2 As shown, statistical analysis was performed on the evaluation data to calculate the main effects of each implementation scheme for each core component, as well as the interaction effects between different core components. In some embodiments, the main effect is the independent average impact of the implementation scheme of a single core component on the overall performance of the framework while keeping other core components constant; the interaction effect is the additional performance impact generated by a specific implementation combination of multiple core components that exceeds the sum of the main effects of each core component.

[0038] Furthermore, a multi-factor analysis of variance model was constructed based on the collected evaluation data. When calculating the main effects of the planning components, the 16 complete core component configuration schemes were divided into two groups (8 in each group) according to planning variants, and the average performance differences between the two groups were compared. The interaction effect was calculated as the difference between the actual performance of a specific component combination and the expected performance based on the main effects of each component. For example, if the actual performance of the combination of planning component variant A1 and memory component variant B2 is significantly higher than the expected performance of the sum of their main effects, then there is a positive interaction effect. Confidence intervals and significance tests were used to determine the statistical reliability of the effect.

[0039] Specifically, the method for quantifying the main effect is as follows: Let the total number of complete core component configuration schemes be N=2. k Where k is the number of core components, in this embodiment k=4, therefore N=16. For each core component F i (i=1,2,3,4), which includes two implementation schemes, denoted as follows: and .make Indicates all containing The average performance metrics (such as accuracy) of the configuration schemes (N / 2=8 in total) under specific task domains and underlying models. Indicates all containing The average performance metrics of the eight configuration schemes. Then, the core component F... i Main effect E Fi The calculation formula is: E Fi = - ; If E Fi >0 indicates that the following is adopted Implementation scheme relative to It has a positive gain; if E Fi <0 indicates It has a negative impact. To assess the statistical significance of the main effect, an independent samples t-test was performed on the two groups of performance data, and the p-value was calculated. The main effect was considered statistically significant when p < 0.05.

[0040] The quantitative calculation method for interaction effects is as follows: For any two core components F i and F j(i≠j), consider four combinations of implementation schemes: ( , ), ( , ), ( , ), ( , ).make Indicates simultaneous inclusion and The average performance of the configuration schemes (N / 4 = 4 in total). Then F i With F j Second-order interaction effect I Fi,Fj The calculation formula is: ;

[0041] The physical meaning of this formula is: F i Does the effect depend on F? j It changes depending on the level. When I Fi,Fj An interaction effect is considered to exist when the variance is not equal to 0 and the result reaches a significant level (p < 0.05) after analysis of variance. The sign of the interaction effect indicates the direction of cooperation: a positive value indicates... and Combinations produce super-superposition synergistic enhancement effects; negative values ​​indicate that the combination produces mutual inhibition effects. Similar methods can be extended to calculate third-order and higher-order interaction effects.

[0042] This step quantifies the main effect and interaction effect to accurately decompose the independent contribution and synergistic impact of components, breaking through the limitation of traditional overall evaluation that cannot be attributed, and providing a quantitative basis for dependency identification.

[0043] S6. Based on the main effect and interaction effect data, identify the implementation scheme dependencies of each core component and the domain dependencies of each implementation scheme; Among them, the implementation scheme dependency of the core component refers to the characteristic that two implementation schemes of the same core component produce significantly different or opposite performance effects under the same experimental conditions; the domain dependency of the implementation scheme refers to the characteristic that the same implementation scheme of the same core component exhibits opposite performance effects in different task domains.

[0044] In some embodiments, the criteria for determining scheme dependence are: the performance difference between two variants of the same core component under at least one experimental condition is opposite in sign to the overall main effect and the difference is statistically significant; the criteria for determining domain dependence are: the main effects of any variant of the same core component in different task domains are opposite in sign and both are statistically significant; the detected dependence phenomena are presented in the form of a structured table, including detailed information such as the components involved, variants, conditions, effect sizes, and confidence intervals.

[0045] Specifically, the quantitative identification method for implementation scheme dependency is as follows: For each core component F i First, calculate its main effect globally (across all domains and models). Then, for each specific experimental condition C (the condition is uniquely determined by "task domain + underlying model"), the main effects under that condition are calculated. If at least one experimental condition C exists... If both of the following conditions are met, then the core component Fi is determined to have an implementation dependency: (1)sign( )≠sign( That is, the sign of the local main effect is opposite to the sign of the global main effect; (2)∣ |>δ and p<0.05, where δ is the preset effect size threshold (in this embodiment, δ=5% accuracy difference).

[0046] The specific meaning of opposite signs is: if the global main effect is positive (indicating...) Overall superior However, under condition C The lower main effect is negative (indicating) On the contrary, it is better than If the negative difference exceeds a threshold and is statistically significant, then an implementation scheme dependency is determined to exist. The strength of the dependency is determined by | - | Quantification.

[0047] Specifically, the method for quantitative identification of domain dependencies is as follows: For each core component F i Each implementation scheme (e.g.) For different task domains D (mathematical reasoning, code generation, information retrieval, and multi-step tool usage), the domain main effect of the implementation scheme in each domain is calculated. First, the average performance contribution of the implementation scheme in domain D is calculated: fixing all possible combinations of the other three core components (total 2...). k -1 =8 types), calculate Compared to Average performance difference in this area: ;

[0048] in This represents the average performance in domain D, using Fi+ and fixing other components to a specific combination.

[0049] If there are two different domains D aand D b If the following conditions are met simultaneously, then the implementation scheme Fi+ is determined to have domain dependency: (1)sign( )≠sign( That is, the main effect signs of the implementation scheme are opposite in the two domains; (2)∣ - |>δ and the main effects in both domains satisfy p<0.05, where δ is a preset threshold (in this embodiment, δ=5% accuracy difference).

[0050] The specific meaning of opposite symbols is: the same implementation scheme in domain D a It produces a positive contribution (better than the other variant) in domain D. b The lower variant produces a negative contribution (inferior to the other variant), and both effects in opposite directions reach statistical significance. The strength of domain dependence is determined by | - | Quantification.

[0051] The detected implementation dependencies and domain dependencies are output in a structured table format. The table fields include: component name, implementation variant, condition / domain, local main effect value, global main effect value, sign difference, effect size difference, p-value, confidence interval (95% CI), and dependency determination conclusion.

[0052] This step accurately identifies implementation solution dependencies and domain dependencies, revealing the strong correlation between component effects and solutions and domains, and providing key support for avoiding trial and error and scientifically configuring the framework.

[0053] S7, such as Figure 2 As shown, based on the dependency identification results, the optimal component configuration of the intelligent agent operation framework that is suitable for the enterprise's target tasks is selected, and the framework configuration optimization is completed.

[0054] In some embodiments, the dependency and configuration optimization module outputs the detected dependency phenomena and optimization suggestions in a structured table format, including the components involved, variants, applicable task domains, effect sizes, confidence intervals, and recommended component configuration combinations.

[0055] This step outputs the optimal configuration based on data patterns, realizing the transformation of framework configuration from experience-driven to data-driven, significantly improving the adaptability, stability and performance of the intelligent agent in the target scenario, and reducing the trial and error costs for enterprises.

[0056] The present invention provides an enterprise AI agent operation framework component evaluation and configuration optimization system, which includes a modular agent framework, a factorial experiment configuration controller, a multi-domain multi-model evaluation engine, a statistical effect analysis module, a dependency detection and configuration optimization module, and a report generation module connected in sequence. The modular intelligent agent framework has four independently pluggable and replaceable component slots, corresponding to the task planning component, information memory component, execution feedback component, and context management component, respectively, allowing for flexible assembly of variants of each core component. The component slots of the modular intelligent agent framework support the extension of new component types and implementation schemes through standard interfaces, automatically updating the fully factorial experimental design matrix. Furthermore, adding new components or variants only requires implementation according to the standard interface specification and registration in the framework, and the system automatically updates the experimental design matrix.

[0057] The factorial experiment configuration controller is responsible for generating configuration schemes for each complete core component, scheduling and managing the execution process of 16 complete core component configuration schemes; The multi-domain, multi-model evaluation engine connects multiple underlying large language models; it automatically executes the configuration schemes of each complete core component across models and task domains, and collects performance data. In some embodiments, the multi-domain, multi-model evaluation engine supports distributed deployment, enabling parallel execution of evaluation tasks with different complete core component configuration schemes, and supports breakpoint resumption and result aggregation of evaluation tasks. Specifically, the evaluation engine supports distributed deployment, enabling the parallel execution of evaluation tasks with different configuration schemes using multiple servers, and the collected performance data can be exported as HTML interactive reports and PDF static reports.

[0058] The statistical effect analysis module calculates the main effects of each implementation scheme of each core component and the interaction effects between different core components; The dependency detection and configuration optimization module identifies the implementation dependencies of each core component and the domain dependencies of each implementation, filters the optimal component configuration of the intelligent agent running framework that is adapted to the enterprise's target tasks, and outputs the optimal component configuration scheme. The report generation module is responsible for generating structured evaluation reports and configuration optimization reports.

[0059] In the context of standardized AI infrastructure for cloud service providers, this invention can provide standardized framework configuration suggestions for AI Agent hosting services on cloud platforms such as AWS, Azure, and Google Cloud. Based on the evaluation data of this invention, cloud service providers can offer validated pre-configured agent templates to customers in different industries, reducing the complexity of framework configuration for customers, while continuously optimizing their Agent-as-a-Service product lines through component effect data.

[0060] In vertical domain agent customization scenarios, this invention can guide the construction of specialized intelligent agent frameworks in fields such as law, healthcare, and finance. Different domains have significantly different requirements for the components of the intelligent agent: the legal domain may require more robust planning components to organize complex legal reasoning, the healthcare domain needs more reliable feedback components to validate diagnostic recommendations, and the financial domain requires more precise context management to handle data-intensive analysis. The evaluation results of this invention can directly guide each domain in selecting the component implementation scheme most suitable for its task characteristics.

[0061] In enterprise AI agent operation platform scenarios, this invention can be used to diagnose and optimize performance bottlenecks in deployed AI agent systems. Enterprise AI operations teams can use the cause-effect ablation method of this invention to perform component-level performance attribution for agents in the production environment: identifying which component implementation is the performance bottleneck and which component implementation actually produces negative effects in the current task domain, thereby enabling targeted component replacement and upgrades.

[0062] In AI agent development platform scenarios, this invention can serve as a component selection tool for intelligent agent frameworks such as LangChain and AutoGPT. Developers face numerous component implementation choices when building AI agents: multi-step decomposition planning versus thought chain planning, vector retrieval memory versus summary memory, LLM feedback versus rule feedback, etc. The factorial evaluation method of this invention provides a quantitative performance assessment for each component implementation combination, helping developers select the validated optimal component configuration based on the target task type (mathematical reasoning, code generation, etc.), avoiding blind trial and error.

[0063] Example: This embodiment focuses on an enterprise-level general AI agent operating framework, selecting four core components: task planning, information memory, execution feedback, and context management. Each component is configured with two differentiated implementation schemes. Through a full factorial experimental design, a large-scale evaluation of 16 configurations is completed, quantitatively analyzing the main effects and interaction effects of the components, identifying implementation scheme dependencies and domain dependencies, and ultimately providing the optimal component configuration for the enterprise agent.

[0064] (1) Hardware and software environment Hardware: 2 servers, each configured with a 32-core CPU, 128GB of memory, and 2×NVIDIA A100 80GB graphics cards; The underlying large language model uses three capability gradient models: high-capability LLM (such as GPT-4 closed-source model / Tongyi Qianwen flagship version), medium-capability LLM (such as Llama 3 70B), and basic-capability LLM (such as Llama 3 8B). Software framework: Python 3.10, LangChain 0.2.x, FastAPI, Pandas, SciPy; Task dataset: Covering 4 domains, with a total of 290 task instances: 1) Mathematical Reasoning: 100 multi-step word problems (including addition, subtraction, multiplication, division, proportion, and equations); 2) Code generation: 80 Python function writing tasks (data processing, simple algorithms); 3) Information retrieval: 60 multi-hop question-and-answer tasks (cross-document, multi-condition query); 4) Tool Invocation: 50+ steps to invoke tool tasks (check weather, check date, calculator combination).

[0065] (2) Core components and implementation scheme 1) Task planning component Variant A1 (Multi-step decomposition programming / ReAct style): Process: Receive task → Break down into an ordered list of subtasks → Loop through "Think → Use tools → Observe results → Update status" until task is completed; Implementation: Use Prompt to force the output of structured steps (e.g., [{"step1":"...","step2":"..."}]), execute and verify each step.

[0066] Variant B1 (Implicit Programming with Mind Chain): Process: Receive task → Prompt guides the model to complete "planning + reasoning + execution" in one go, without explicit subtask decomposition; Implementation: Add "Please think step by step first, and then give the final execution result" to the Prompt, and directly output the final answer / action.

[0067] 2) Information Memory Component Variant A2 (Complete Historical Memory): Implementation: Maintain a global list to store all user inputs, model outputs, and tool return results. Each context is concatenated with the entire history without truncation. Variant B2 (Summary Memory): Implementation: Set a threshold of 4000 characters. If the cumulative historical length is ≤4000 characters, store the entire record. If it exceeds 4000 characters, call LLM to generate a 100-200 character summary, and only retain the summary plus the latest 3 rounds of interaction records.

[0068] 3) Execution feedback component Variant A3 (Direct Feedback Transmission): Implementation: The tool call result / environment feedback is directly appended into the context as a string, without any formatting processing; Variant B3 (Structured Feedback Parsing): Implementation: Use regular expressions and JSON parsing to extract key fields (such as {"status":"success","data":"...","error":null}), format them, and then pass them into the model.

[0069] 4) Context Management Component Variant A4 (Fixed Template Context): Template: "[System Prompt] {Character Settings}\n[Interaction History] {Complete History}\n[Current Task] {User Input}\nPlease Output Result:", fixed structure remains unchanged; Variant B4 (Dynamic Context Clipping): Implementation: Dynamically adjust based on the current task type: prioritize retaining calculation processes for mathematical tasks, prioritize retaining function definitions for code tasks, prioritize retaining query keywords for retrieval tasks, automatically prune irrelevant content, and control the total length of the context to ≤6000 characters.

[0070] (3) Design and evaluation process of factorial experiments Generate 16 configurations: 4 core components × 2 variants = 16 combinations. Example configuration: A1 (planning) + B2 (memory) + A3 (feedback) + B4 (context). Experimental scheduling: Use a scheduling script to traverse 16 configurations, each configuration is run in 3 LLMs × 4 domains, and each task is repeated 3 times and the average is taken; Metrics collection: Record four core metrics: task completion rate, answer accuracy, average number of steps, and time spent on a single reasoning session; Data scale: 16 complete core component configurations × 3 models × 4 domains × 3 replicates = 576 sets of experiments, with a total of 2300+ valid data points collected.

[0071] (4) Statistical analysis and dependency identification Main effect calculation: Taking the "task planning component" as an example, the 16 configurations are divided into group A1 (8 types) and group B1 (8 types). The difference in average accuracy between the two groups is the main effect of the planning component. Interaction effect calculation: Calculate the actual accuracy of the "A1+B2" combination, subtract the sum of the main effects of A1, B2, and the baseline accuracy, and the difference is the interaction effect; Dependency determination: Implementation dependency: Two variants of the same component have an accuracy difference of ≥10% under the same conditions and are in opposite directions (e.g., A1 math +15%, code -8%, B1 the opposite). Domain dependence: The same variant has an accuracy difference of ≥12% between two domains and the directions are opposite (e.g., B2 memory is +18% in retrieval and -11% in mathematics).

[0072] (5) Experimental results and optimal configuration Solution dependency: The task planning components have strong dependencies—A1 (decomposition planning) has a 12%-18% higher accuracy rate in mathematical / tool-based tasks, while B1 (mind chain) has a 10%-15% higher accuracy rate in code / retrieval tasks; Domain dependence: Summary memory (B2) performs well in long text retrieval tasks (+20%), but poorly in short math tasks (-13%). Optimal configuration (general enterprise use): Planning A1 + Memory B2 + Feedback B3 + Context B4, with an overall accuracy improvement of 14.7% and a 22% reduction in the number of steps, suitable for most enterprise office and data processing scenarios.

[0073] (6) System deployment and implementation The optimal configuration is packaged into an enterprise intelligent agent template and deployed to the enterprise AI platform, allowing users to call it directly. At the same time, an evaluation interface is opened, allowing enterprises to upload their own task sets and quickly complete component-level evaluation and configuration tuning.

[0074] Advantages of this invention: This invention, by modularly disassembling the core components of the intelligent agent's operating framework and employing a fully factorial experimental design, can systematically and quantitatively analyze the independent main effects and inter-component interaction effects of the implementation schemes of each core component, accurately identify the implementation scheme dependencies of each core component and the domain dependencies of each implementation scheme, and effectively overcome the shortcomings of existing technologies that can only evaluate overall performance and cannot achieve component-level attribution analysis.

[0075] Based on large-scale, reproducible experimental data and statistical analysis results, this invention provides an objective, scientific, and quantitative basis for the selection and configuration optimization of components in the enterprise AI agent operating framework. It avoids the traditional configuration method that relies on experience and blind trial and error, and significantly improves the operational stability, inference efficiency, and task completion quality of the agent in different task domains.

[0076] This invention constructs a modular, pluggable, and automated standardized evaluation system that supports large-scale evaluation across multiple underlying large language models and various task domains in parallel. It significantly reduces the technical threshold, manpower cost, and time cost for enterprises to conduct component-level performance evaluation, and improves evaluation efficiency and result reliability.

[0077] This invention has good versatility and scalability, supports the addition of new core component types and implementation scheme variations. It is not only applicable to the evaluation and optimization of general enterprise AI intelligent agent operating frameworks, but also can be quickly adapted to the development and optimization of special intelligent agents in vertical fields such as law, medicine, and finance. It has a wide range of application scenarios and outstanding practical value.

[0078] The above are merely preferred embodiments of the present invention and are not intended to limit the present 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. A method for evaluating and optimizing the configuration of components in an enterprise AI intelligent agent operating framework, characterized in that, include: The intelligent agent operation framework is broken down into four independently controllable core components: task planning component, information memory component, execution feedback component, and context management component. For each core component, at least two implementation schemes with substantial technical differences are designed, with each implementation scheme corresponding to a different technical route and design logic; Based on all implementation schemes of the four core components, a full factorial experimental design matrix is ​​constructed to generate a variety of complete core component configuration schemes. Multiple underlying large language models with different capability levels were selected, and each configuration scheme of the complete core components was independently evaluated in multiple task domains, including mathematical reasoning, code generation, information retrieval, and multi-step tool usage domains. Evaluation data was collected, including accuracy, completion rate, and step efficiency. Statistical analysis was performed on the evaluation data to calculate the main effects of each implementation scheme of each core component, as well as the interaction effects between different core components; Based on the main effect data and the interaction effect data, the implementation scheme dependency of each core component and the domain dependency of each implementation scheme are identified; wherein, the implementation scheme dependency of the core component refers to the characteristic that two implementation schemes of the same core component produce significantly different or opposite performance effects under the same experimental conditions; the domain dependency of the implementation scheme refers to the characteristic that the same implementation scheme of the same core component exhibits opposite performance effects in different task domains. Based on the dependency identification results, the optimal component configuration of the intelligent agent operation framework that is suitable for the enterprise's target tasks is selected, and the framework configuration optimization is completed.

2. The method for evaluating and optimizing the configuration of enterprise AI intelligent agent operation framework components according to claim 1, characterized in that, Two implementation schemes are designed for the task planning component: Variant A1 adopts multi-step decomposition planning, which explicitly decomposes the task into an ordered list of subtasks and executes them step by step; Variant B1 adopts mind chain implicit planning, which completes planning and execution in a single reasoning through prompting engineering guidance model.

3. The method for evaluating and optimizing the configuration of enterprise AI intelligent agent operation framework components according to claim 1, characterized in that, Two implementation schemes are designed for the information memory component: Variant A2 is a complete history memory that retains all interaction history data; Variant B2 is a summary memory that automatically summarizes and compresses historical data that exceeds a preset length for storage.

4. The method for evaluating and optimizing the configuration of enterprise AI intelligent agent operation framework components according to claim 1, characterized in that, Two implementation schemes are designed for the execution feedback component: Variant A3 is direct feedback transmission, transmitting tool call results and environmental feedback signals as is; Variant B3 is structured feedback parsing, formatting the feedback results and extracting key information before transmission.

5. The method for evaluating and optimizing the configuration of enterprise AI intelligent agent operation framework components according to claim 1, characterized in that, Two implementation schemes are designed for the aforementioned context management component: Variant A4 is a fixed template context, which uses a predefined template to organize the context content; Variant B4 is a dynamic context pruning, which adjusts the context content in real time according to the task running status.

6. The method for evaluating and optimizing the configuration of enterprise AI intelligent agent operation framework components according to claim 1, characterized in that: The main effect is the independent average impact of the implementation scheme of a single core component on the overall performance of the framework, while keeping other core components constant; the interaction effect is the additional performance impact generated by a specific implementation combination of multiple core components, which exceeds the sum of the main effects of each core component.

7. A system for evaluating and optimizing the configuration of enterprise AI agent operating framework components, implementing the method for evaluating and optimizing the configuration of enterprise AI agent operating framework components as described in any one of claims 1 to 6, characterized in that: It includes a modular intelligent agent framework, a factorial experiment configuration controller, a multi-domain multi-model evaluation engine, a statistical effect analysis module, a dependency detection and configuration optimization module, and a report generation module, which are connected in sequence. The modular intelligent agent framework has four independently pluggable and replaceable component slots, corresponding to the task planning component, information memory component, execution feedback component and context management component, respectively, allowing for flexible assembly of variants of each core component; The factorial experiment configuration controller is responsible for generating configuration schemes for each complete core component, scheduling and managing the execution flow of each complete core component configuration scheme; The multi-domain, multi-model evaluation engine connects multiple underlying large language models, automatically executes the configuration schemes of each complete core component across models and task domains, and collects performance data; The statistical effect analysis module calculates the main effects of each implementation scheme of each core component and the interaction effects between different core components; The dependency detection and configuration optimization module identifies the implementation scheme dependencies of each core component and the domain dependencies of each implementation scheme based on the main effect data and the interaction effect data. It then filters the optimal component configuration of the intelligent agent operation framework that is suitable for the enterprise's target task and outputs the optimal component configuration scheme. The implementation scheme dependency of a core component refers to the characteristic that two implementation schemes of the same core component produce significantly different or opposite performance effects under the same experimental conditions. The domain dependency of an implementation scheme refers to the characteristic that the same implementation scheme of the same core component exhibits opposite performance effects in different task domains. The report generation module is responsible for generating structured evaluation reports and configuration optimization reports.

8. The enterprise AI intelligent agent operation framework component evaluation and configuration optimization system according to claim 7, characterized in that: The modular intelligent agent framework's component slots support the expansion of new component types and implementation schemes through standard interfaces, and automatically update the full factorial experimental design matrix.

9. The enterprise AI intelligent agent operation framework component evaluation and configuration optimization system according to claim 7, characterized in that, The multi-domain, multi-model evaluation engine supports distributed deployment, can execute evaluation tasks with different complete core component configuration schemes in parallel, and supports breakpoint resumption and result aggregation of evaluation tasks.

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