Enterprise agent-oriented capability self-adaptive assembly and model matching method and system

Abstract

The application discloses an enterprise intelligent agent capability adaptive assembly and model matching method and system, comprising: mapping a natural language business target into a demand characteristic vector of a unified capability-demand space; constructing an inherent capability boundary vector and a cost vector for a stepped candidate model; constructing a compensable capability vector for a tool plug-in, a knowledge base and a workflow template, modeling marginal compensation gain as a conditional quantity dependent on the residual capability of the selected model in the dimension; merging the model inherent capability and the component conditional quantity into a comprehensive supply capability in a saturated aggregation manner, jointly solving the model selection and component combination under the constraint that the gap does not exceed the tolerance; presenting a coverage atlas for user confirmation to complete the instantiation of the intelligent agent; and collecting running effect data to iteratively update the modeling parameters. The application eliminates the capability redundancy and gap generated by serial decision making, and improves the configuration precision and cost performance.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to a method and system for adaptive assembly and model matching of capabilities for enterprise intelligent agents. Background Technology

[0002] With the continuous opening up and commercialization of large language model capabilities, agent technology is moving from academic prototypes to enterprise-level application deployments. Enterprises are increasingly creating agents for specific business objectives in a self-service manner, covering various scenarios such as work order processing, report generation, data querying, and customer service follow-up. Users typically describe their business objectives in natural language and expect the platform to automatically generate a working agent configuration. A complete agent configuration consists of two types of decisions: first, selecting the base model to host the agent from a candidate model system; and second, equipping the agent with external capability components, including tool plugins, associated knowledge bases, and workflow orchestration templates. These two types of decisions jointly determine what tasks the agent can handle and at what cost during actual operation, representing the core engineering problem of enterprise agent platforms.

[0003] To balance domain depth, response latency, and resource consumption, the industry's candidate model system is evolving from a single, general-purpose large model to a tiered model system. This system progresses from general-purpose models to industry-specific models to vertical models, with generality decreasing and domain depth and specialization increasing progressively. Furthermore, different tiers of models exhibit significant differences in their inherent capability boundaries in areas such as reasoning ability, domain knowledge, and knowledge timeliness. For example, vertical models have internalized the professional knowledge and terminology of a specific domain, possessing strong inherent capabilities in the domain-specific dimension; while lightweight general-purpose models, although low-cost to deploy, have inherent weaknesses in complex reasoning and long reasoning chains. This creates a crucial coupling relationship between model tiers and external capability components: the inherent capability boundaries of the selected model tier directly determine which dimensions of deficiency external capability components need to compensate for; and the compensation effect provided by the available capability components, in turn, influences the tier of model required for sufficiency. However, current technologies lack effective modeling of this coupling relationship, resulting in isolated and uncoordinated decisions regarding model selection and component assembly.

[0004] Existing technologies have developed along two separate paths, neither of which has resolved the aforementioned coupling problem. The first path involves runtime large-model routing techniques, including cost-aware routing, semantic routing, and cascading routing. These methods operate at runtime, on a request-by-request basis, selecting the most cost-effective model for each incoming request for an agent with a fixed and deployed toolset. However, this presupposes that the capability components are already determined, thus never addressing the issue of determining model tiers in a coupled manner with component assembly during the assembly phase, nor how to determine the matching scheme between models and components in the absence of runtime data. Another type of academic work attempts to retrieve tools or agents in a shared vector space, but this still requires runtime data and only addresses semantic matching between queries and tools, without explicitly modeling the inherent capability boundaries of the model. The second path involves low-code or no-code agent building platforms. While these platforms offer capabilities for model selection and the assembly of tools, knowledge bases, and workflows, model selection and the assembly of various capability components are independent, user-driven, and separately managed configuration steps. The platform does not jointly solve for whether the union of the model's inherent capabilities and the component's compensating capabilities precisely covers business requirements. Although publicly available enterprise-level intelligent agent creation platforms cover model selection, action assembly, and iterative improvement based on user feedback, their model selection and capability assembly remain governance-oriented and separate options. They do not model the inherent capability boundaries of the tiered model and the compensable capabilities of the components into the same requirement space for consistent and collaborative solving.

[0005] When model selection and capability component assembly are performed as two independent, sequential decisions, two types of mismatches systematically arise. The first is capability redundancy, where redundant, homogeneous components are repeatedly assembled into the required dimensions already covered by a specific vertical or industry model, resulting in unnecessary resource waste and increased system complexity. The second is capability gaps, where a lightweight model chosen for latency or overhead considerations lacks a specific inherent capability that is not compensated for by any assembled component. This gap goes unnoticed during the assembly phase and is only discovered retrospectively when the agent fails or experiences quality degradation after going live. The crux of the problem is the lack of a technical capability to integrate the model's inherent capability boundaries and the compensable capabilities of components into the same space, and to solve them jointly and consistently to achieve precise requirement coverage. In particular, the dependency that component marginal compensation gains tend to saturate in dimensions fully covered by the model, while gains are more significant in dimensions with weaker model capabilities, has not yet been effectively modeled, leading to the long-term neglect of the coupling between model tier and component assembly at the solution level.

[0006] This problem is particularly acute in the unique scenario of non-expert users creating intelligent agents themselves. In this situation, the user making the request only states the business objective in natural language and lacks the professional ability to judge the inherent capability boundaries of a particular model level. Therefore, the redundancy and gaps caused by decoupling decisions are invisible and uncorrectable at the human level. At the same time, since the intelligent agent does not yet exist, the platform cannot detect this mismatch retrospectively using any runtime feedback or execution trajectory. Unlike runtime request-based routing scenarios that deal with a deployed intelligent agent with a fixed toolset, the model level selection and capability component assembly in the enterprise self-creation scenario must be completed in one go without any runtime data, and the decision-maker is unaware of the model's capability boundaries. This combination of constraints—cold start, one-time molding, and capability boundaries imperceptible to the decision-maker—makes existing runtime routing technologies and manual discrete assembly platforms ineffective. There is an urgent need for a specialized technical means that can consistently and collaboratively determine model level selection and capability component assembly as a coupled whole during the assembly stage, where only natural language business objectives are used as input. This would eliminate capability redundancy and gaps under cold start conditions and present the assembly scheme in a user-friendly manner for non-experts, enabling users without capability boundary judgment capabilities to effectively participate in confirmation and adjustment. Summary of the Invention

[0007] To address the technical problem of how to consistently and collaboratively determine the two coupled decisions—the selection of tiered model levels and the assembly of tool plugins, knowledge bases, and workflow capability components—during the cold start assembly phase where only natural language business objectives are input and the agent is not yet running, this invention provides a capability adaptive assembly and model matching method and system for enterprise agents. Its core concept lies in constructing a unified multi-dimensional capability-requirement space, representing the multi-dimensional requirements of business objectives, the inherent capability boundaries of tiered models, and the compensable capabilities of each capability component on the same dimension of this space. Then, using this space as a carrier, a joint and consistent solution is performed on the coupled model level selection and capability component assembly, ensuring that the union of the model's inherent capabilities and the component's compensable capabilities precisely covers the multi-dimensional requirements implicit in the business objectives, without creating capability redundancy or leaving capability gaps. Furthermore, the marginal compensation gain of components in each dimension is modeled as a conditional quantity dependent on the residual capabilities of the selected model, thereby explicitly characterizing the coupling relationship between the model and components at the modeling level.

[0008] In one aspect, the present invention provides a capability adaptive assembly and model matching method for enterprise intelligent agents, the method comprising the following steps.

[0009] Step S1 involves extracting multi-dimensional semantic features from the user's business objectives described in natural language, mapping them to a requirement feature vector on a unified capability-requirement space. This unified capability-requirement space is comprised of capability dimensions including task complexity, domain expertise, real-time requirements, and interaction patterns. Specifically, the system receives the user's business objectives described in natural language, extracts multi-dimensional semantic features from the objective description, and maps them to a requirement feature vector defined on the unified capability-requirement space. The capability dimensions include at least task complexity, domain expertise, real-time requirements, and interaction patterns, and preferably also include inference chain length, knowledge timeliness, tool call density, and data sensitivity to more precisely characterize the multi-dimensional requirements implied by the business objectives. Furthermore, in step S1, each component of the demand feature vector is a demand intensity value within a normalized interval for the corresponding capability dimension. Step S1 also includes: extracting demand intensity dimensionally from the business objective using an analytical large model combined with a structured prompt template, and calibrating the extraction results using preset anchor point examples; adding a confidence level to each demand intensity value, with capability dimensions having a confidence level below a threshold being handled with a wider tolerance in the solution of step S4. Here, calibrating the dimensionally extracted results using preset anchor point examples can effectively reduce scale drift between different business objectives, making demand intensity values ​​comparable within a unified interval; while applying a wider tolerance to dimensions with low confidence levels avoids the uncertainty of semantic extraction being rigidly transmitted to subsequent solution constraints.

[0010] Step S2 involves constructing an inherent capability boundary vector representing the upper bound of capabilities that the general model, industry model, and vertical model in the tiered candidate model system can provide without external components, along with a cost vector, on the same capability dimension. Specifically, for each candidate model in the tiered candidate model system, including the general model, industry model, and vertical model, an inherent capability boundary vector is constructed on the same capability dimension as in step S1. Each component of the inherent capability boundary vector represents the upper bound of capabilities that the candidate model can inherently provide without any external components in the corresponding capability dimension. Simultaneously, a cost vector including inference latency and resource overhead is constructed for each candidate model. Further, in step S2, the inherent capability boundary vector is obtained statistically from the benchmark detection results of the candidate models in each capability dimension, or initialized by the capability profile declared by the model vendor and subsequently corrected by feedback. The inherent capability boundary components of the industry model and vertical model in the domain specialization dimension are higher than those of the general model, while the inherent capability boundary components of the general model in the task complexity dimension are lower than those of the industry model and vertical model. The cost vector includes inference latency and resource overhead. Preferably, the capability boundary components of industry models and vertical models in dimensions such as domain specialization and knowledge timeliness are significantly higher than those of lightweight general models, while the capability boundary components of lightweight general models in dimensions such as task complexity and inference chain length are relatively lower. This accurately depicts the inherent capability differences formed by the gradual arrangement of models at each level from general to vertical in the same space.

[0011] Step S3 involves constructing compensable capability vectors for each capability component of the tool plugin, knowledge base, and workflow template on the same capability dimension. The marginal compensation gain of each capability component in each capability dimension is modeled as a conditional quantity related to the residual capability of the selected model in that capability dimension, ensuring that the marginal gain of the capability component saturates in strong dimensions of the model and increases in weak dimensions. Specifically, for each capability component in the preset tool plugin library, knowledge base resource pool, and workflow template library, a compensable capability vector and an introduced cost are constructed on the same capability dimension. Each component of the compensable capability vector represents the marginal capability gain that the component can provide for the corresponding capability dimension. Further, in step S3, the compensable capability vector of the knowledge base has higher components in the domain specialization and knowledge timeliness dimensions than in other dimensions; the compensable capability vector of the tool plugin has higher components in the real-time requirements and tool call density dimensions than in other dimensions; and the compensable capability vector of the workflow template has higher components in the task complexity and inference chain length dimensions than in other dimensions. Therefore, the marginal compensation gain of the component is modeled as a conditional quantity related to the residual capability of the selected model in that dimension. That is, the marginal gain of the component tends to saturate in dimensions where the model is already strong, and its marginal gain is more significant in dimensions where the model is weak. This explicitly characterizes the dependence of the component's compensation effect on the model level selection at the modeling level, forming a coupling term between the model and the component. Preferably, in step S3, the compensation of the capability component in the first capability dimension is modeled as changing the effective residual of the selected model in the second capability dimension. This introduces a coupling matrix between capability dimensions into the aggregation of the comprehensive supply capability, making the solution in step S4 a joint solution of indivisible capability dimensions. This coupling matrix further characterizes cross-dimensional compensation effects such as knowledge compensation indirectly alleviating complex reasoning.

[0012] Step S4: Aggregate the inherent capability boundary components of the candidate model and the conditional quantities of the selected component subset in a saturated manner to form the comprehensive supply capacity. Under the constraint that the coverage gap in each capability dimension does not exceed a preset tolerance, solve for the model selection and component combination that minimizes the sum of the capability redundancy weighted sum, model cost, and component cost, thus obtaining the capability assembly scheme. Specifically, under a given candidate model and a given component subset, the comprehensive supply capacity in each capability dimension is defined as the result of a saturated aggregation of the inherent capability boundary components of the model in that dimension and the conditional compensation gains of each component in that subset in that dimension. Furthermore, in each capability dimension, the coverage gap is defined as the portion where the demand intensity exceeds the comprehensive supply capacity, and the capability redundancy is defined as the portion where the comprehensive supply capacity exceeds the demand intensity. The objective of the solution is to minimize the sum of the capability redundancy weighted sum in each dimension, model cost, and component cost, while satisfying the constraint that the coverage gap in each dimension does not exceed a preset tolerance. Preferably, the solution of step S4 is implemented using a two-layer structure: the outer layer enumerates candidate models one by one in the tiered model levels. For each enumerated candidate model, the residual requirement is obtained by subtracting the inherent capability boundary vector from the requirement feature vector dimension by dimension; the inner layer, based on the residual requirement, greedily selects capability components in a weighted set coverage manner according to the ratio of the marginal compensation gain of the capability component in the dimension with the remaining gap to its introduced cost, until the residual requirements in each dimension are filled, obtaining the component subset under that level; the outer layer then compares the comprehensive cost of each candidate model together with its inner component subset, and selects the model and component combination with no coverage gap and the lowest comprehensive cost as the capability assembly scheme. Thus, the model level selection determines the residual requirement, the residual requirement determines the component assembly, and the total component cost, in turn, determines the comprehensive cost-effectiveness of that level and participates in cross-level comparisons, so that the two originally coupled decisions are consistently and collaboratively determined in the same space. As an optional technique, the inner layer solution is replaced by one of branch and bound, integer programming relaxation, or local search; when the number of model tiers exceeds a preset value, the outer layer enumeration is replaced by pruning search in ascending order of cost. Thus, even with a large number of tiers, a near-optimal capability assembly scheme can still be obtained within acceptable computational overhead.

[0013] Step S5 presents the capability assembly scheme and its coverage map for user confirmation or adjustment, thereby completing the instantiation of the intelligent agent. Specifically, the capability assembly scheme, along with its coverage map on each capability dimension, is presented to the user through a visual interface for confirmation or adjustment. After user confirmation, the instantiation of the intelligent agent is automatically completed according to the determined model selection, parameter configuration, and capability component combination. Further, in step S5, the coverage map indicates the demand intensity, the comprehensive supply capacity, and the remaining coverage gaps and capability redundancy in a capability-dimensional manner, explicitly externalizing the redundancy and gaps as objects for user confirmation or adjustment. When the user adjusts the model level or adds or deletes capability components, step S4 is triggered to re-solve under the constraints imposed by the user, ensuring that the capability assembly scheme maintains that the coverage gaps in each capability dimension do not exceed the preset tolerance. For the special case of non-expert users creating intelligent agents on their own, the coverage map explicitly externalizes the previously invisible redundancy and gaps, enabling users who do not have the ability to judge the boundary of the model to intuitively perceive and confirm or adjust accordingly, thereby keeping the solution in a state of just the right coverage.

[0014] Step S6 involves collecting operational performance data of the created agents and iteratively updating the extraction and calibration parameters of the inherent capability boundary vector, the conditional quantities, and the demand feature vector. Further, in step S6, the operational performance data includes task completion quality, actual capability performance across various capability dimensions, response latency, and resource consumption. After correcting the inherent capability boundary vector and the conditional quantities based on the operational performance data, steps S1 to S5 are re-executed for subsequently received business objectives. Thus, by initially addressing cold starts with only prior capability profiles, the modeling of capabilities within the same space is gradually corrected using accumulated operational feedback, continuously improving the exact coverage accuracy of subsequent agent capability assembly and model matching.

[0015] Another aspect of the present invention provides a capability adaptive assembly and model matching system for enterprise intelligent agents, characterized by comprising: a target semantic parsing module, used to extract multi-dimensional semantics from business objectives described by users in natural language, mapping them to requirement feature vectors on a unified capability-requirement space, wherein the unified capability-requirement space is spanned by capability dimensions including task complexity, domain specialization, real-time requirements, and interaction modes; a tiered model matching module, used to construct inherent capability boundary vectors representing the upper bounds of capabilities that can be provided without external components for general models, industry models, and vertical models in a tiered candidate model system on the same capability dimension, and construct cost vectors; and a capability assembly recommendation module, used to construct compensable capability vectors for each capability component of tool plugins, knowledge bases, and workflow templates on the same capability dimension, and to assign the capability components to each capability... The marginal compensation gain model of a dimension is a conditional quantity related to the residual capability of the selected model in that capability dimension. This saturates the marginal gain of the capability component in the strong dimension of the model and increases the marginal gain in the weak dimension. It is also used to aggregate the inherent capability boundary components of the candidate model and the conditional quantities of the selected component subset in a saturated manner into a comprehensive supply capability. Under the constraint that the coverage gap in each capability dimension does not exceed a preset tolerance, the model selection and component combination that minimizes the sum of the capability redundancy weighted sum, the model cost, and the component cost are solved to obtain the capability assembly scheme. The agent instantiation module is used to present the capability assembly scheme and its coverage map for user confirmation or adjustment, thereby completing the instantiation of the agent. The matching strategy optimization module is used to collect the running effect data of the created agent and iteratively update the extraction calibration parameters of the inherent capability boundary vector, the conditional quantities, and the demand feature vector. Each module corresponds one-to-one with the aforementioned method steps S1 to S6. The target semantic parsing module performs the multi-dimensional demand feature extraction described in step S1. The hierarchical model matching module performs the model inherent capability boundary and cost modeling described in step S2 and participates in the joint solution in step S4. The capability assembly recommendation module performs the component compensable capability modeling described in step S3 and performs the coupled joint solution described in step S4 to generate a capability assembly scheme that exactly covers the target. The agent instantiation module performs the visualization presentation, confirmation adjustment and instantiation creation described in step S5. The matching strategy optimization module performs the running effect feedback iteration described in step S6. The functional description of each module is consistent with the recording method of the corresponding method steps.

[0016] This invention unifies the multidimensional requirements of business objectives, the inherent capability boundaries of a tiered model, and the compensable capabilities of each capability component onto the same dimension of a multidimensional capability-demand space. It explicitly constructs coupling terms between the model and components by modeling component compensation gains as conditional quantities dependent on the residual capabilities of the selected model. Then, using exact coverage as the criterion, it performs a one-time, unified solution for model tier selection and capability component assembly. This fundamentally eliminates two types of mismatches systematically caused by decoupled serial decision-making: it avoids capability redundancy caused by repeatedly assembling heavy, homogeneous components for requirement dimensions already internalized and covered by vertical or industry models, and it avoids capability gaps caused by downgrading to lightweight models where missing inherent capabilities are not compensated by any assembled components. This solution can be completed in the cold start stage with only natural language objectives, before the agent is instantiated and without any operational data, without relying on any runtime feedback or execution trajectory. This aligns with the unique situation of non-expert users creating agents independently in a one-time process. Furthermore, by utilizing a coverage map that identifies demand intensity, comprehensive supply capacity, and residual coverage gaps and capability redundancy across dimensions, this invention explicitly externalizes redundancies and gaps that are previously invisible and uncorrectable to non-expert users into identifiable and adjustable objects. This allows users without the ability to judge model capability boundaries to intuitively perceive and participate in decision-making, and triggers a re-solution under constraints when users make adjustments to ensure the solution always provides precise coverage. In addition, by collecting operational performance data from created agents and iteratively updating the inherent capability boundary vector, component condition compensation gain, and demand extraction calibration parameters, this invention continuously corrects capability modeling in the same space based on the initial cold start using prior capability profiles. This allows the capability assembly and model matching accuracy of subsequent agents to continuously improve with the accumulation of operational feedback, thereby significantly improving the precise coverage and overall cost-effectiveness of agent configuration while reducing the overall cost of models and components. Attached Figure Description

[0017] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the capability adaptive assembly and model matching method for enterprise intelligent agents provided in the embodiments of the present invention, showing the execution order of steps S1 to S6 and the data flow between them.

[0019] Figure 2 This is a data flow and modeling diagram of the unified capability-demand space provided in the embodiments of the present invention. It shows the unified representation of the demand feature vector, the inherent capability boundary vector of each candidate model, the compensable capability vector of each capability component on the same capability dimension, and the dependence of the component conditional compensation gain on the residual capability of the selected model.

[0020] Figure 3This is a schematic diagram of the two-layer structure of the coupled joint solution in step S4 provided in the embodiment of the present invention, which shows the nesting and feedback relationship between the outer model gear enumeration and the inner weighted set covering greedy assembly.

[0021] Figure 4 This is a structural block diagram of the capability adaptive assembly and model matching system for enterprise intelligent agents provided in an embodiment of the present invention. It shows the target semantic parsing module, the hierarchical model matching module, the capability assembly recommendation module, the intelligent agent instantiation module, and the matching strategy optimization module, as well as their connection relationships.

[0022] Figure 5 This is a cross-sectional schematic diagram of the exact coverage relationship within the unified capability-demand space provided by the embodiments of the present invention. It shows the correspondence between the inherent capability of the selected model, the sum of the conditional compensation gains of the assembled components, the comprehensive supply capability and the demand intensity in each capability dimension, as well as the two deviation scenarios of capability redundancy and coverage gap.

[0023] Figure 6 This is a comparison chart of eight-dimensional demand intensity and comprehensive supply capacity coverage provided by an embodiment of the present invention, showing a comparison of the coverage of demand intensity, the comprehensive supply capacity of the present invention and the comprehensive supply capacity of the serial baseline in eight capacity dimensions.

[0024] Figure 7 This is a family of saturation curves showing the change of conditional compensation gain with model residual capability provided in the embodiments of the present invention. It illustrates the dependence and saturation characteristics of normalized marginal compensation gain on model residual capability under different saturation rate parameters. Detailed Implementation

[0025] To make the objectives and technical solutions of this invention clearer, the embodiments of this invention will be further described in detail below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of this invention and should not be construed as limiting the scope of protection of this invention.

[0026] Example 1: This example provides a capability adaptive assembly and model matching method for enterprise intelligent agents, such as... Figure 1As shown, the method is executed during the cold start assembly phase, where the user describes their business objective in natural language, the agent has not yet been instantiated, and the platform has no operational data. It includes steps S1 to S6. For ease of understanding, this embodiment first provides a complete explanation of the execution (reasoning) phase of the method, i.e., how to process a specific natural language business objective input and produce a capability assembly scheme based on existing prior capability modeling (steps S1 to S5). Then, it provides a separate explanation of the analytical model training and capability modeling calibration phase supporting this execution phase, i.e., how to obtain the aforementioned requirement extraction model and various capability vectors from sample data (including the feedback iteration described in step S6). The two phases are presented in separate paragraphs.

[0027] Before the execution phase begins, the unified capability-requirement space adopted in this embodiment is defined. This space is spanned by several preset capability dimensions. This embodiment uses eight capability dimensions, denoted as d=1,2,…,8, which are, in order, task complexity, domain specialization, real-time requirements, interaction mode, inference chain length, knowledge timeliness, tool call density, and data sensitivity. The first four are mandatory basic dimensions, and the latter four are preferred refined dimensions to more precisely depict the multidimensional requirements implied by business objectives. The values ​​on each dimension are normalized to the interval [0,1], where 0 indicates no requirement or capability on that dimension, and 1 indicates that the requirement or capability on that dimension has reached the upper bound. Requirements, inherent capabilities of the model, and compensable capabilities of components located in the same space are therefore comparable dimension by dimension. This is the basis for subsequent coupled joint solution, such as… Figure 2 As shown.

[0028] Step S1 involves performing multi-dimensional semantic extraction on the user's business objective described in natural language, mapping it to a demand feature vector in a unified capability-demand space. Specifically, the platform receives the user's input description of the business objective, such as "build an assistant for the after-sales team that can handle work order classification and regular follow-ups," and the target semantic parsing module performs multi-dimensional semantic feature extraction on this description, outputting a demand feature vector r=(r_1,r_2,…,r_8), where each component r_d is the demand intensity value taking values ​​in the range [0,1] on the corresponding capability dimension.

[0029] In implementation, this embodiment employs a large analytical model combined with structured prompt templates to extract demand intensity dimension by dimension. The structured prompt templates provide a definition, scoring criteria, and several anchor point examples for each capability dimension, guiding the large analytical model to output an intensity estimate between 0 and 1 for the current business objective in that dimension, along with a brief explanation. For example, for the "domain expertise" dimension, the template states "0 represents general daily tasks, 1 represents the need for in-depth industry knowledge and terminology," and provides anchor point examples such as "casual conversation → 0.1" and "structured medical records → 0.9." By calibrating the dimension-by-dimensional extraction results using preset anchor point examples, scaling drift between different business objectives can be effectively reduced, ensuring that demand intensity values ​​from different sources and with different expressions are comparable within a unified range. After extraction, the above "after-sales assistant" example has the following characteristics: task complexity is approximately 0.55, domain expertise is approximately 0.60, real-time requirement is approximately 0.45, interaction mode is approximately 0.50, reasoning chain length is approximately 0.50, knowledge timeliness is approximately 0.65, tool call density is approximately 0.58, and data sensitivity is approximately 0.70.

[0030] Furthermore, this embodiment adds a confidence score c_d∈[0,1] to each demand intensity value to characterize the reliability of the extraction result for that dimension. The confidence score can be obtained by inversely mapping the dispersion of the intensity obtained by the analytical large model from multiple extractions of the same business objective under different prompting perturbations; the smaller the dispersion, the higher the confidence score. For capability dimensions with a confidence score lower than a preset threshold (0.6 in this embodiment), a wider tolerance is applied in the solution of step S4, that is, the upper bound of the coverage gap constraint on that dimension is relaxed. This treatment avoids the uncertainty of semantic extraction being rigidly transmitted to the subsequent solution constraints, so that the solution retains the necessary flexibility in extracting uncertain dimensions, while strictly approximating exact coverage in extracting certain dimensions. As an alternative, the confidence score can also be given directly by the analytical large model as an additional output, or estimated based on the tightness of the anchor point sample hits.

[0031] Step S2: For the general model, industry model, and vertical model in the hierarchical candidate model system, construct inherent capability boundary vectors on the same capability dimensions, and construct cost vectors. Specifically, for each candidate model k in the hierarchical candidate model system, the hierarchical model matching module constructs an inherent capability boundary vector b^(k)=(b_1^(k),…,b_8^(k)) on the same eight capability dimensions as in step S1. Each component b_d^(k)∈[0,1] represents the upper bound of the capability inherently provided by the candidate model in the corresponding capability dimension without any external components. At the same time, a cost vector is constructed for the candidate model. In this embodiment, the cost vector includes two components: inference latency and resource overhead. The model cost C_model^(k) of the model is obtained by weighted normalization of the two components.

[0032] The intrinsic capability boundary vector can be obtained in two ways. One is baseline probing: offline evaluation of candidate models is performed using a standardized set of probing tasks covering each capability dimension, and the corresponding components are obtained based on their pass rate or score statistics on each dimension's probing tasks. The other is profile initialization: each component is directly initialized by the capability profile declared by the model vendor, and corrected through runtime feedback in subsequent step S6. This embodiment initializes newly accessed models with profiles first, and then covers them with probing results, balancing access efficiency and characterization accuracy.

[0033] The tiered model system is arranged progressively from general models to industry models to vertical models, with their inherent capability boundaries exhibiting regular differences. Preferably, the inherent capability boundary components of industry models and vertical models are significantly higher than those of lightweight general models in dimensions such as domain expertise and knowledge timeliness, because they have internalized the relevant domain expertise and terminology during the training phase. In contrast, lightweight general models have relatively lower inherent capability boundary components in dimensions such as task complexity and inference chain length, exhibiting a natural weakness in complex reasoning. For example, in this embodiment, the boundary vector of a certain lightweight general model is approximately 0.40 in the domain expertise dimension and approximately 0.45 in the task complexity dimension; a certain industry model is approximately 0.78 in the domain expertise dimension; and a certain vertical model is approximately 0.88 in the domain expertise dimension and approximately 0.72 in the knowledge timeliness dimension. In this way, the inherent capability differences formed by the progressive arrangement of models from general to vertical are accurately depicted in the same space, providing a unified basis for subsequently determining whether a certain requirement dimension should be undertaken by model internalization or component compensation.

[0034] Step S3: For each capability component in the tool plugin library, knowledge base, and workflow template, construct a compensable capability vector on the same capability dimension, and model the marginal compensation gain of the component in each capability dimension as a conditional quantity related to the residual capability of the selected model in that dimension. Specifically, for each capability component m in the preset tool plugin library, knowledge base resource pool, and workflow template library, the capability assembly recommendation module constructs a compensable capability vector g^(m)=(g_1^(m),…,g_8^(m)) and an introduction cost C_comp^(m) on the same eight capability dimensions, where the component g_d^(m) represents the marginal capability gain that the component can provide for the corresponding capability dimension.

[0035] Different types of components possess differentiated compensation expertise across various dimensions. Preferably, the compensability vector of the knowledge base has a higher weighting in the dimensions of domain expertise and knowledge timeliness than in other dimensions; the compensability vector of the tool plugin has a higher weighting in the dimensions of real-time requirements and tool call density than in other dimensions; and the compensability vector of the workflow template has a higher weighting in the dimensions of task complexity and inference chain length than in other dimensions. Thus, the associated knowledge base primarily compensates for domain expertise and knowledge timeliness, the tool plugin primarily compensates for real-time requirements and tool call density, and the workflow orchestration template primarily compensates for task complexity and inference chain length, clearly defining the compensation expertise of the three types of components within the same space.

[0036] The key to this step is that the marginal compensation gain of a component is not a fixed constant, but is modeled as a conditional quantity related to the residual capability of the selected model in that dimension. Let the residual capability of the selected model k in dimension d be the margin of its inherent capability relative to the demand (i.e., b_d^(k) − r_d, where a positive value indicates that the inherent capability of the model has exceeded the demand, and a negative value indicates that there is a gap in that dimension). Then, the actual marginal gain of component m in that dimension is denoted as Δ_d^(m,k) = g_d^(m)·φ(b_d^(k),r_d), where the conditional factor φ is a decreasing function of the model's existing capability. When the model is strong in that dimension (b_d^(k) approaches or exceeds r_d), φ tends to a smaller value, and the marginal gain of the component tends to saturate; when the model is weak in that dimension (b_d^(k) is much lower than r_d), φ tends to a larger value, and the marginal gain of the component is more significant. Thus, the dependence of the component compensation effect on the model level selection is explicitly characterized at the modeling level, constituting a coupling term between the model and the component, such as... Figure 2 As shown.

[0037] Furthermore, as a preferred embodiment, the compensation of the component in the first capability dimension can be modeled as changing the effective residual of the selected model in the second capability dimension, thereby introducing a coupling matrix Φ between capability dimensions in the aggregation of comprehensive supply capabilities, making the solution in step S4 a joint solution that is indivisible between capability dimensions. This coupling matrix characterizes cross-dimensional compensation effects such as "compensation of rich related knowledge in the domain expertise dimension can indirectly alleviate the burden of complex reasoning in the task complexity dimension," that is, knowledge compensation indirectly alleviates complex reasoning. In a simplified implementation where it is not necessary to characterize cross-dimensional effects, Φ can be taken as an identity matrix, making the compensation of each dimension independent of each other, thereby reducing the solution complexity.

[0038] Step S4: Aggregate the inherent capability boundary components of the candidate model and the conditional quantities of the selected component subset into a comprehensive supply capacity in a saturation manner. Under the constraint that the coverage gap in each capability dimension does not exceed the preset tolerance, solve for the model selection and component combination that minimizes the sum of the capability redundancy weighted sum, model cost, and component cost, thus obtaining the capability assembly scheme. Specifically, for a given candidate model k and a given component subset S, define the comprehensive supply capacity on each capability dimension d as s_d(k,S)=sat(b_d^(k)+Σ_{m∈S}Δ_d^(m,k)), where sat(·) is a saturation function (such as taking a cutoff of 1 or soft saturation) to reflect the marginal decrease of capability superposition and avoid supply overshoot. Furthermore, define the coverage gap gap_d=max(0,r_d−s_d) and capability redundancy red_d=max(0,s_d−r_d) on each dimension, such as... Figure 5 As shown, the sum of the inherent capabilities of the selected models and the component conditional compensation gains in each capability dimension constitutes the comprehensive supply capacity. The excess segment relative to the demand intensity is the capability redundancy, and the insufficient segment is the coverage gap. The objective of the solution is to minimize the weighted sum of capability redundancy Σ_d w_d·red_d in each dimension and the sum of model cost C_model^(k) and component cost Σ_{m∈S}C_comp^(m) under the constraint that the coverage gap_d of each dimension does not exceed the preset tolerance τ_d, i.e., min{Σ_d w_d·red_d+λ·(C_model^(k)+Σ_{m∈S}C_comp^(m))}, where w_d is the dimension weight and λ is the cost trade-off coefficient; for dimensions determined to have low confidence in step S1, their tolerance τ_d is relaxed accordingly.

[0039] Preferably, the solution is implemented using a two-layer structure, such as... Figure 3As shown. The outer layer enumerates candidate models k one by one in the tiered model levels; for each enumerated candidate model, the residual demand ρ_d=max(0,r_d−b_d^(k)) is obtained by subtracting its inherent capability boundary vector b^(k) from the demand feature vector r dimension by dimension, that is, the residual demand is the non-negative gap amount obtained by taking the absolute value of the aforementioned residual capability when it is negative. For this residual demand, the inner layer greedily selects capability components in a weighted set coverage manner based on the criterion of the ratio of the marginal compensation gain of the capability component to its introduced cost in the dimension with remaining gaps Δ_d^(m,k) / C_comp^(m): in each round, the component that maximizes the ratio of the current uncovered residual filling amount to the cost is added to the subset S and the residuals of each dimension are updated, and this is repeated until the residual demand of each dimension is filled and no significant redundancy is introduced, thus obtaining the minimum redundancy-cost component subset under this level. The outer layer then compares the comprehensive cost C_model^(k)+Σ_{m∈S}C_comp^(m) of each candidate model together with its inner component subset, and selects the one with the lowest comprehensive cost among all schemes without coverage gaps as the final capability assembly scheme.

[0040] Therefore, the model tier selection determines the residual requirements, the residual requirements determine the component assembly, and the total component cost, in turn, determines the overall cost-effectiveness of that tier and participates in cross-tier comparisons, ensuring that the two originally coupled decisions are consistently and collaboratively determined within the same space. Continuing with the aforementioned "after-sales assistant" example, if the outer layer enumerates a certain industry model, and its domain expertise dimension has reached 0.78, covering a requirement of 0.60, then the inner layer will not equip that dimension with a heavy domain knowledge base, avoiding capability redundancy. However, its requirement in the tool call density dimension is only about 0.40, below 0.58, so the inner layer equips a tool plugin to fill this gap, avoiding capability gaps. The final solution precisely covers the requirements of each dimension, without redundancy or gaps. As an optional technical means, the inner greedy solution can be replaced by one of branch and bound, integer programming relaxation, or local search to obtain a better solution on a small-scale component library; when the number of model tiers exceeds a preset value, the outer enumeration can be replaced by pruning search in ascending order of cost, that is, once the cost of a certain tier model exceeds the comprehensive cost of the current best complete solution, the branch and higher tiers are pruned, so that even when the tier size is large, a near-optimal capability assembly solution can still be obtained within an acceptable computational cost.

[0041] Step S5 presents the capability assembly scheme and its coverage map for user confirmation or adjustment, thereby completing the instantiation of the intelligent agent. Specifically, the intelligent agent instantiation module presents the capability assembly scheme obtained in step S4, along with its coverage map on each capability dimension, to the user through a visual interface. The coverage map simultaneously indicates the demand intensity r_d, the comprehensive supply capacity s_d, and the remaining coverage gap gap_d and capability redundancy red_d in a capability-dimensional manner (such as a radar chart or a parallel bar chart), explicitly externalizing the redundancy and gap that were originally invisible to non-expert users into identifiable and adjustable objects.

[0042] This externalization is significant for the unique scenario of non-expert users creating intelligent agents themselves: the user making the request lacks the expertise to judge the inherent capability boundaries of a particular model level. Using coverage maps, they can intuitively perceive whether the solution is "sufficient" or "redundant" in each dimension without needing to understand the internal mechanisms of the model and components, and can then confirm or adjust accordingly. When the user adjusts the model level (e.g., requesting a downgrade due to budget considerations) or adds or removes capability components, step S4 is triggered to re-solve under user-imposed constraints. For example, under the constraint of locking a certain model level, the inner component assembly is re-executed, ensuring that the resulting capability assembly solution maintains coverage gaps in each capability dimension within the preset tolerance under user constraints, i.e., always maintaining perfect coverage. After user confirmation, the platform automatically instantiates the intelligent agent according to the determined model selection, parameter configuration, and capability component combination, outputting a runnable intelligent agent instance.

[0043] The above describes the execution (inference) phase of the method. The following section explains the analytical model training and capability modeling calibration phase that supports this execution phase. This phase is completed offline before the execution phase to obtain the requirement extraction and calibration capabilities used in step S1, and the various capability vectors and condition factors used in steps S2 and S3. After the system goes live, it is continuously iterated and updated by step S6.

[0044] Regarding the demand extraction capability of the large analytical model used in step S1, this embodiment obtains the ability to extract demand intensity dimension by dimension through supervised fine-tuning based on the general large model. The training data consists of several business objective descriptions and their manually labeled eight-dimensional demand intensity ground truth and confidence scores. During preprocessing, irrelevant noise is removed from the text and it is unified into the input form of a structured prompt template. The intensity ground truth is normalized to [0,1]. Fine-tuning uses the AdamW optimizer with a batch size of 24. The initial learning rate is set to 2.3×10^-4 and decayed to 2.0×10^-6 using cosine annealing. The number of training epochs is used as the convergence criterion when the mean absolute error of dimension extraction on the validation set no longer decreases (in this embodiment, convergence occurs after approximately 7 epochs). The loss function is the weighted sum of the smoothed L1 loss between the predicted and ground truth values ​​of demand intensity in each dimension and the binary cross-entropy of the confidence term. Evaluation metrics include the mean absolute error dimensionally, anchor point sample hit consistency, and ranking correlation coefficient. Once training is complete, an analytical model is obtained that can stably extract demand intensity and attach confidence scores dimension by dimension during the execution phase.

[0045] Regarding the inherent capability boundary vector in step S2, the compensable capability vector in step S3, the condition factor φ, and the coupling matrix Φ, their calibration does not depend on end-to-end backpropagation, but is obtained by fitting the baseline detection statistics and a small number of calibration samples: the inherent capability boundary component is initialized by the statistical results of the candidate model on each dimension detection task; the shape parameter of the condition factor φ (controlling the rate of decay as the model has acquired capability) and the compensable capability component of the component are obtained by dimensional regression fitting of the difference between the actual gain of "model plus component" on the calibration task and the performance of "model alone", so that the fitted Δ_d^(m,k) naturally tends to saturate in the strong dimension of the model and naturally increases in the weak dimension of the model.

[0046] Step S6: Collect operational performance data of the created agents and iteratively update the extraction calibration parameters of the inherent capability boundary vector, conditional quantities, and demand feature vectors. Specifically, the matching strategy optimization module collects operational performance data of the created agents in actual operation. This operational performance data includes at least task completion quality, actual capability performance in each capability dimension, response latency, and resource consumption. Based on this data: for the inherent capability boundary vector, an exponential sliding correction is made to the prior boundary components based on actual capability performance; for the component conditional compensation gain, the relevant parameters of φ and g are updated by regression using "the actual improvement in this dimension after assembling the component"; for the demand extraction calibration parameters, the anchor points and scale of the analytical model are updated with incremental feedback samples that have been manually confirmed or adjusted. After the correction is completed, steps S1 to S5 are re-executed for the subsequently received business objectives. In this way, based on the initial cold start response using only prior capability profiles, the system gradually corrects the capability modeling in the same space with accumulated operational feedback, continuously improving the exact coverage accuracy of subsequent agent capability assembly and model matching, thereby continuously improving the overall cost-effectiveness of agent configuration while reducing the overall cost of models and components.

[0047] Example 2: In a preferred embodiment of the present invention, the conditional compensation gain modeling of the compensable capability vector of the component and its cross-dimensional coupling matrix extension are used as the core technical means to further refine steps S3 and S4, so as to achieve precise coupling and joint solution of model level selection and capability component assembly in a unified capability-demand space.

[0048] Capability-Demand Space Dimensional Configuration This implementation selects eight capability dimensions to form a unified capability-requirement space, namely: task complexity (dimensional 1), domain specialization (dimensional 2), real-time requirements (dimensional 3), interaction mode (dimensional 4), inference chain length (dimensional 5), knowledge timeliness (dimensional 6), tool call density (dimensional 7), and data sensitivity (dimensional 8). The value of each dimension is normalized to a closed interval [0, 1], where 0 indicates that the agent's business objectives have almost no requirements for that dimension, and 1 indicates that the requirement intensity for that dimension is the highest. The eight-dimensional space covers the main types of enterprise business requirements while keeping the solution scale within an acceptable range for engineering. When a specific industry scenario requires a more granular capability characterization, it can be expanded to up to twelve dimensions as needed. However, when the number of dimensions exceeds twelve, the computational cost of the greedy inner layer will increase accordingly. It is recommended to evaluate whether to introduce dimension-weighted preprocessing based on dimension importance.

[0049] In step S1, the system calls the parsing large model and, in conjunction with the structured prompt template, independently extracts the demand intensity value d_i (i=1,2,...,8) and its confidence score conf_i for each dimension of the business objective. The prompt template pre-sets four anchor point examples for each capability dimension. The anchor point examples consist of typical business description text and their corresponding manually calibrated demand intensity values, covering four representative demand levels [0.1, 0.4, 0.7, 0.9]. The parsing large model uses these four anchor point examples as references in the current context to interpolate the newly input business objective description to generate continuously valued demand intensity values ​​d_i. If the confidence score conf_i of a certain dimension is lower than the preset threshold of 0.5, it indicates that the information in the business description for that dimension is insufficient to support accurate extraction. The coverage constraint tolerance for that dimension in the subsequent step S4 is relaxed from the standard tolerance τ to 1.5τ to prevent the semantic extraction uncertainty from being rigidly transmitted into unsatisfiable solution constraints.

[0050] Mathematical modeling of conditionally compensated gain This embodiment performs the following conditional modeling of the marginal compensation gain of the capability component in each dimension in step S3.

[0051] Let cap_i(m) be the intrinsic capability boundary component of the enumerated candidate model m in capability dimension i. Then the residual capability of the model in dimension i is defined as: res_i(m) = cap_i(m) − d_i When res_i(m) > 0, the model's inherent capabilities in this dimension exceed the requirements, and no external component compensation is needed; when res_i(m) < 0, there is a capability gap, which needs to be filled through component compensation.

[0052] For the capability component c, its conditional marginal compensation gain gain_i(c, m) on dimension i is modeled as the following saturated response function: gain_i(c, m) = g0_i(c) × sigma(−α × res_i(m)) Where g0_i(c) is the baseline compensation capability of component c on dimension i, with a value in [0, 1], initialized by historical operating data statistics or component supplier declaration; sigma(x) = 1 / (1 + exp(−x)) is the logistic function; α is the saturation rate parameter, with a preferred value range of 2.0 to 5.0. The specific recommended value is related to the business sensitivity of the capability dimension—for dimensions that are more sensitive to capability level, such as real-time performance (dimension 3) and tool call density (dimension 7), α should be larger (e.g., 4.0 to 5.0) so that the gain decays rapidly when the model meets the requirements; for dimensions with relatively mild compensation effects, such as domain expertise (dimension 2) and knowledge timeliness (dimension 6), α should be smaller (e.g., 2.0 to 3.0).

[0053] The key property of this modeling is that when res_i(m) is much greater than zero (the model's inherent capability in dimension i far exceeds the requirement), sigma(−α × res_i(m)) approaches zero, and the marginal gain of the component in this dimension tends to saturate and decay; when res_i(m) is much less than zero (there is a large gap), sigma(−α × res_i(m)) approaches one, and the component can fully utilize its baseline compensation capability g0_i(c). Therefore, the marginal compensation gain of the component explicitly depends on the residual capability of the selected model at the modeling level, forming the mathematical core of the coupling between model level and component assembly.

[0054] Cross-dimensional compensation coupling matrix Based on the independent conditional modeling of each dimension, this implementation further introduces a cross-dimensional compensation coupling matrix K, where K is an 8×8 real-valued matrix and the element κ_ij(c) represents the degree of influence of component c on the effective residual of the model in dimension j (j≠i) after compensation is applied in dimension i.

[0055] Specifically, when component c is added to a subset of components, its correction to the effective residual of dimension j is defined as: res_j_eff(m, c) = res_j(m) + κ_ij(c) × gain_i(c, m) Where κ_ij(c) takes values ​​in the range of [−1, 1]: a positive value indicates that the compensation of the component in dimension i brings a positive improvement to the effective residual of dimension j (positive overflow), and a negative value indicates that the component in dimension i introduces a negative interference that causes an effective residual in dimension j.

[0056] The following illustrates a representative example of cross-dimensional coupling relationships. The structured compensation of the workflow template in the task complexity dimension (dimension 1) objectively reduces the logical depth required for single-step inference by decomposing complex tasks into ordered sequences of steps, thereby indirectly improving the effective residual in the inference chain length dimension (dimension 5); κ_15 is preferably initialized to a positive value, with a suggested range of 0.2 to 0.4. The introduction of the knowledge base in the domain expertise dimension (dimension 2) reduces the internal long-chain inference performed by the model during inference to compensate for insufficient domain knowledge, indirectly reducing the pressure on the inherent capacity of the inference chain length (dimension 5); κ_25 is preferably initialized to 0.1 to 0.3. The high-frequency invocation of real-time sensitive tool plugins (dimension 7), while compensating for real-time requirements (dimension 3), may increase data transmission paths, causing a slight negative interference to the effective residual in the data sensitivity dimension (dimension 8); κ_78 is preferably initialized to −0.1 to −0.3. Dimension pairs in matrix K without clear semantic relationships are initialized to zero to maintain a sparse structure.

[0057] The runtime update of the coupling matrix K is combined with step S6: using the difference between the measured performance of the deployed agents in each dimension and the comprehensive supply capacity predicted by the model as the residual, κ_ij is iteratively corrected using stochastic gradient descent, with a learning rate preferably between 0.001 and 0.01. Since the physical meaning of each κ_ij requires its absolute value to not exceed 1, a projection constraint is added to the update step. When the number of historical deployment samples for a certain type of component is less than thirty, the confidence level of the corresponding κ_ij estimate is low. It is recommended to temporarily fix it at the initial value and start the data-driven update after sufficient data accumulation to avoid the estimation bias caused by a small number of samples being amplified to the solution constraints.

[0058] After introducing the coupling matrix, the aggregation of comprehensive supply capacity is no longer a dimensionally separable independent summation, but a joint aggregation of inter-dimensional influences: when assembling the component subset S sequentially into the model m, each time a component c is added, the effective residuals of the other dimensions need to be updated according to the cross-dimensional coupling relationship, until the effective residuals of the entire subset converge in each dimension, and then the coverage gap is evaluated using the final effective residuals. Among them, the residuals to be filled in each dimension are converted from their effective residuals, i.e., Δ_j=max(0, −res_j_eff), and only the dimensions whose effective residuals are still negative (i.e., there are still gaps) are retained; when positive overflow causes the effective residual of a certain dimension to turn from negative to positive, its residuals to be filled decrease to zero.

[0059] Specific implementation of the two-level solution The outer layer enumerates candidate model tiers in ascending order of overall model cost, processing lightweight general-purpose models, medium-sized industry models, and large-scale vertical models in that order. For each enumerated candidate model m_k, the residual requirements for each dimension are calculated: Δ_i(m_k) = max(0, d_i − cap_i(m_k)) Only dimensions with capability gaps are retained. Dimensions already covered by the inherent capabilities of m_k have their residual requirements set to zero and are no longer involved in the selection of inner components.

[0060] For the residual requirement Δ(m_k), the inner layer selects the capability components using the following iterative greedy process: First, initialize the residuals to be filled in each dimension to Δ_i(m_k), forming a set of dimensions to be covered. Second, iterate through all components in the candidate component library, calculating the weighted marginal benefit of each component c_j in the current set of dimensions to be covered. This is defined as the sum of conditional gains (including indirect gains from cross-dimensional overflows) of the component across all gap dimensions, multiplied by the demand intensity weight of the corresponding dimension, and then divided by the introduction cost cost(c_j) of the component. Select the component with the highest weighted marginal benefit to cost ratio, add it to the current component subset, and iteratively update the effective residuals of the remaining dimensions according to the cross-dimensional coupling matrix, thereby converting their residuals to be filled. If the residual to be filled in a certain dimension drops to zero due to positive overflow, remove it from the set of dimensions to be covered. Repeat the above process until the set of dimensions to be covered is empty (all gap dimensions are filled) or the weighted marginal benefits of all components in the candidate component library drop to zero.

[0061] The outer layer records the comprehensive cost of each candidate model m_k and its corresponding component subset S_k: cost(m_k) + sum(cost(c_j) for c_j in S_k). Among the candidate schemes that satisfy the constraint that "the coverage gap in each dimension does not exceed the preset tolerance", the scheme with the lowest comprehensive cost is selected as the final capability assembly scheme. When the lightweight general model can completely fill the residual requirements through precise component compensation, and the sum of the component introduction costs is lower than the inherent capability cost added by the upgraded model, the outer layer tends to choose the scheme of combining the lightweight model with precise components; this situation precisely reflects the cost optimization capability of this method compared to simply relying on the model upgrade strategy.

[0062] When the number of candidate model tiers exceeds the preset value, the outer enumeration can be replaced by a pruning search in ascending order of cost: if a certain tier model is not higher than a certain lower cost model that has been processed (i.e., it is dominated by the latter) in all dimensions of the inherent capability boundary component, then skip that tier, so as to control the computational cost of the outer enumeration in a large-scale candidate model system and keep the overall solution completed within an engineering-acceptable time.

[0063] Beneficial effects: This embodiment introduces a cross-dimensional compensation coupling matrix, upgrading the aggregation of comprehensive supply capabilities from independent calculations of each dimension to joint aggregation of mutual influence between dimensions, resulting in the following technical effects.

[0064] On the one hand, the coverage accuracy of capability assembly has improved. When modeling only with independent conditions on a dimension-by-dimensional basis, the indirect improvement effect of workflow templates on the inference chain length dimension by compensating for task complexity cannot be quantified and included in the overall supply. This leads to the system tending to underestimate the overall supply capacity in scenarios with cross-dimensional positive spillover, thus assembling one or more components that are not actually necessary. After introducing the coupling matrix, this type of indirect effect is explicitly modeled and participates in the calculation of coverage constraints. The selection of component subsets is able to perceive this type of cross-dimensional effect, and capability redundancy is reduced to a certain extent.

[0065] On the other hand, cross-dimensional constraint propagation allows the component decision in one dimension to naturally affect the constraint state of other dimensions during the inner greedy selection process. Thus, without introducing exhaustive search, a more coordinated component selection result is obtained than the independent greedy approach for each dimension, reducing the probability of getting trapped in local optima due to ignoring the correlation between dimensions.

[0066] Furthermore, the coupling coefficient κ_ij is continuously updated using the operational results data from step S6. The cross-dimensional compensation relationships inherent in the historical deployment data are systematically accumulated, allowing the accuracy of subsequent agent capability assembly to gradually improve with data accumulation. It should be noted that when the number of historical deployment samples for a certain type of capability component in a specific business scenario is insufficient, the reliability of the corresponding coupling coefficient estimation is relatively low, and the aforementioned accuracy improvement effect will be somewhat affected in such scenarios.

[0067] Principle Analysis The underlying technical principle that enables this embodiment to achieve the aforementioned beneficial effects is as follows.

[0068] In the conditional compensation gain function gain_i(c, m) = g0_i(c) × sigma(−α × res_i(m)), the choice of the logistic function as the saturation response curve has the following significance: its first derivative reaches its maximum value near the zero of the independent variable (i.e., res_i(m) = 0, which is the equilibrium point where the model's inherent capability exactly equals the demand), meaning that the component selection decision is most sensitive to changes in gain when the model's capability and demand are exactly balanced; when the model's inherent capability is much higher than the demand, the gain quickly approaches zero without setting a separate cutoff condition; when the model's inherent capability is much lower than the demand, the gain approaches the upper limit of the component's baseline capability without increasing indefinitely. This adaptive saturation response characteristic allows the same mathematical framework to cover the entire range of scenarios from deep gaps to slight redundancy without case-by-case processing.

[0069] The cross-dimensional coupling matrix K characterizes the following facts from the perspective of functional systems: In real-world enterprise business scenarios, capability dimensions are not isolated but form a functional coupling network through the specific functional characteristics of components. Taking workflow templates as an example, their structured compensation for the task complexity dimension can indirectly improve the inference chain length dimension because the structured workflow decomposes complex tasks into several smaller steps, reducing the inference depth required by the model in each step, and objectively alleviating the pressure on the inherent capability of the inference chain length. Matrix K quantifies this functional association through the κ_ij coefficient, enabling joint solution to consider both direct compensation and indirect spillover effects within a unified mathematical framework, without needing to design separate rules or branch logic for each cross-dimensional effect. As runtime data accumulates, κ_ij continuously approximates the true cross-dimensional compensation coefficient through gradient descent, allowing the model to complete its first solution based on prior initialization during the cold start phase without runtime data in the assembly period. Subsequently, as runtime data accumulates, the coupling coefficient is gradually corrected based on measured feedback, ultimately forming a progressive capability modeling mechanism that integrates prior and data-driven approaches.

[0070] The rationale for the two-layer solution structure lies in the fact that the outer enumeration layer incorporates "model level selection" and "component selection driven by the residual requirements of that level" into a unified comprehensive cost evaluation framework. This ensures that the decisions for both are determined coupled under the same objective function, avoiding the redundancy and gaps systematically generated by the sequential, discrete decision-making process of first selecting the model independently and then selecting the components independently. The inner iterative greedy algorithm, after introducing a cross-dimensional coupling matrix, updates all dimensional residuals affected by the component at each step of component selection. This ensures that the marginal benefit calculation of subsequent components is based on the latest effective residual state, thus incorporating cross-dimensional effects into the greedy ranking decision without significantly increasing computational cost.

[0071] Example 3: In another embodiment of the present invention, a capability coverage matching method for enterprise intelligent agents based on capability knowledge graphs is provided as an optional alternative to the aforementioned multi-dimensional vector space optimization path. This embodiment replaces the continuous vector space with a discretized directed weighted knowledge graph to represent capabilities and requirements, and uses graph coverage matching instead of vector inner product and saturation aggregation for joint solution. It provides a complementary technical path to the aforementioned embodiments when dealing with business requirement scenarios with a clear hierarchical domain structure.

[0072] Construction of a competency knowledge graph This implementation constructs a directed weighted capability knowledge graph G = (V, E), where the node set V contains three types of nodes: requirement concept nodes, model capability nodes, and component function nodes.

[0073] The requirement concept nodes are obtained from a predefined enterprise intelligent agent requirement ontology library. This ontology library is organized hierarchically, refining from top-level general capability categories (such as knowledge retrieval, complex reasoning, real-time data processing, compliance document analysis, multi-turn dialogue management, etc.) down to specific industry-specific capability sub-concepts (such as medical literature evidence-based retrieval, financial regulatory compliance reasoning, industrial equipment real-time parameter monitoring, etc.). Layers are connected by directed edges with an "is-a" hierarchical relationship, ensuring that upper-level nodes semantically encompass the capability categories represented by lower-level nodes. Each requirement concept node is assigned a saliency weight w_r, representing the relative importance of the capability concept within the intelligent agent's business functions. The weight is [0, 1], initialized by manual annotation in the ontology library, and updated based on operational feedback.

[0074] The model capability nodes correspond to the various tiers of models in the hierarchical candidate model system. Each model capability node is connected to the set of demand concept nodes that its inherent capabilities can cover through a directed "satisfaction" edge. The weight s(m, r) of the edge represents the degree to which model m satisfies demand concept node r, and takes a value of [0, 1]. This weight is obtained statistically from the benchmark detection results of each candidate model on the corresponding capability concept, or it is initialized by the capability profile declared by the model vendor and corrected by runtime feedback.

[0075] The component functional nodes correspond to the various capability components in tool plugins, knowledge bases, and workflow templates, and are also connected to the set of requirement concept nodes covered by their compensable capabilities through directed "satisfaction" edges. In addition to satisfaction relationships, component functional nodes are explicitly labeled with two types of relationship edges: one is "dependency" edges, marking that the operation of a component requires another component to be assembled (e.g., a specific workflow template depends on an external tool plugin to complete the data retrieval step before it can be fully executed); the other is "mutual exclusion" edges, marking component pairs that should not be assembled simultaneously (e.g., repeated assembly of similar knowledge bases covering the same domain knowledge scope). The explicit characterization of dependency and mutual exclusion relationships in the graph structure allows the constraints for component selection to be directly read from the graph topology, without the need to maintain additional dependency verification logic during the solution phase.

[0076] Mapping from business objectives to target requirement subgraphs After receiving the user's business objective described in natural language, this implementation method uses a combination of entity recognition and graph query to map the business objective to a target requirement subgraph R_target in the capability knowledge graph.

[0077] Specifically, firstly, named entity recognition is performed on the description of business objectives to extract business domain identifiers (such as "medical", "finance", "manufacturing", etc.), functional verbs (such as "query", "reasoning", "generate", "monitoring", "audit", etc.) and constraint descriptions (such as keywords such as "real-time", "compliance", "private deployment", etc.). Then, the identified entities and functional verbs are retrieved and activated in the capability knowledge graph through a pre-built business vocabulary-requirement concept mapping table to form the target requirement subgraph R_target.

[0078] In the target requirement subgraph, the effective saliency weight w_r of each activated node is adjusted based on the frequency of occurrence of the corresponding entity and function in the business target text and the degree of contextual emphasis, ensuring that key business requirements receive higher priority in subsequent matching. When the business target description is relatively brief or uses expressions outside the ontology vocabulary, resulting in an insufficient number of activated nodes to fully characterize the business intent, this implementation method performs requirement inference along the higher-level concept relationship ("is-a" edge traversal) of the ontology hierarchy, elevating the functional descriptions that cannot be precisely matched to more generalized requirement concept nodes, thus ensuring the coverage completeness of the target requirement subgraph. This higher-level concept expansion strategy has a certain fault tolerance in scenarios where the business target description is not standardized or uses non-standard terminology, but when the expansion level is too deep, it may introduce broad requirement concepts that are not actually needed in the business. It is recommended to set the upper limit of the higher-level concept expansion level to within one-third of the depth of the ontology hierarchy.

[0079] Joint matching solution based on graph cover This implementation transforms the joint solution of model selection and component assembly into a weighted minimum node coverage problem on the target requirement subgraph: under the constraint that the union of the requirement concept nodes that can be covered by the selected model capability nodes and component function nodes through the "satisfy" edge exactly contains all requirement nodes in the target requirement subgraph R_target whose effective saliency weight exceeds a preset threshold, the overall cost of the selected model and component is minimized.

[0080] The solution also adopts a two-layer structure: the outer layer enumerates candidate models in ascending order of comprehensive cost. For each enumerated model m_k, the set of demand nodes C(m_k) that can be covered by the model is found through its "satisfaction" edges, and the set of residual demand nodes that are still not covered in the target demand subgraph is calculated as R_residual = R_target \ C(m_k).

[0081] The inner layer, targeting R_residual, greedily selects components from the component functional node library: prioritizing the selection based on the ratio of the number of residual requirement nodes covered by each component's "satisfy" edges (weighted by node saliency) to the component's introduction cost. This process iteratively selects components until all nodes in R_residual are covered. Each time a component is selected, the dependency completeness of the currently selected component set is simultaneously checked via "dependency" edges. If a selected component's dependent components have not yet been added to the subset, these dependent components are included and their costs are factored in. Simultaneously, candidate components marked by "mutual exclusion" edges that conflict with already selected components are excluded, preventing them from participating in priority calculations for subsequent iterations.

[0082] The outer layer selects the lowest cost combination that can fully cover the target requirement subgraph R_target from the comprehensive costs of each candidate model and its corresponding component subset, and outputs it as the capability assembly scheme.

[0083] Graph structure runtime updates This implementation method iteratively updates the knowledge graph by collecting operational performance data of deployed agents in step S6. The task completion quality scores of deployed agents on the business capabilities corresponding to specific requirement concept nodes are used to correct the "satisfaction" edge weights of model capability nodes and component function nodes: if the measured satisfaction level of a model on a specific requirement node is consistently lower than the current edge weight label value, the corresponding edge weight is lowered; if the measured performance of a component after covering a specific requirement node is consistently better than expected, the corresponding edge weight is increased. Furthermore, when a pattern repeatedly appears in historical deployment data where a certain type of business objective description is always accompanied by the activation of specific requirement concept nodes, new mapping entries can be automatically added to the business vocabulary-requirement concept mapping table, continuously expanding the knowledge graph's coverage of emerging business vocabulary, allowing the graph structure to be progressively updated as the enterprise's business evolves.

[0084] This implementation method has an advantage in matching fine-grained domain requirements when dealing with business needs scenarios with a clearly hierarchical domain structure. This is because the hierarchical ontology of the knowledge graph provides precise hierarchical semantic constraints on professional domain concepts, allowing graph queries to precisely limit the matching range along the ontology hierarchy. However, this implementation method relies on the pre-construction of capability knowledge graphs and requirement ontology libraries, requiring significant initial costs for ontology design and vocabulary organization. When new expressions beyond the ontology vocabulary appear in the business target description, the recall rate of entity recognition may be affected, leading to incomplete coverage of the target requirement subgraph. Therefore, this implementation method is more suitable for deployment in enterprise environments where business scenarios are relatively stable and domain requirement classification can be fully organized in the early stages. Together with the aforementioned implementation based on continuous vector space, it constitutes a complete technical coverage of the present invention for different scenario characteristics.

[0085] Example 4: This example provides a capability adaptive assembly and model matching system for enterprise intelligent agents, such as... Figure 4 As shown, the system includes a target semantic parsing module, a hierarchical model matching module, a capability assembly and recommendation module, an agent instantiation module, and a matching strategy optimization module. Each module is interconnected with a shared capability-demand space storage layer through an internal bus, and data is transmitted sequentially according to the module order.

[0086] In terms of hardware deployment, the system can run on application servers equipped with multi-core processors and large-capacity memory. Each module is deployed independently as a microservice, and asynchronous calls and data transfer between modules are achieved through a lightweight message queue. The shared capability-demand space storage layer is jointly hosted by a relational database and a vector index library. The relational database stores the capability vectors, cost vectors, and calibration parameters of each candidate model and capability component, while the vector index library accelerates the retrieval and comparison of high-dimensional vectors. The system can scale the number of service instances according to the scale of concurrent users. A single application server can meet the concurrent assembly requests of small and medium-sized enterprises, while multiple servers can be configured and uniformly scheduled by a load balancer in large-scale deployment scenarios.

[0087] The target semantic parsing module is used to extract multi-dimensional semantics from the business objectives described by the user in natural language, mapping them to requirement feature vectors on a unified capability-requirement space. This unified capability-requirement space is composed of eight capability dimensions: task complexity, domain specialization, real-time requirements, interaction mode, inference chain length, knowledge timeliness, tool call density, and data sensitivity. Specifically, the target semantic parsing module receives the user's natural language description of the business objective, drives the built-in parsing model to extract requirement intensity values ​​from the description dimension by dimension using structured prompt templates, and calibrates the extraction results through preset anchor point examples to effectively reduce scale drift between different business objectives. Simultaneously, a confidence level is added to each requirement intensity value, and the confidence level and requirement feature vector are jointly passed to the tiered model matching module and the capability assembly recommendation module for subsequent application of greater tolerance in low-confidence dimensions.

[0088] The tiered model matching module is used to construct inherent capability boundary vectors representing the upper bounds of capabilities that can be provided without external components for general models, industry models, and vertical models in the tiered candidate model system, on the same capability dimension, and to construct cost vectors. Specifically, the tiered model matching module maintains a library of inherent capability boundary vectors and a library of cost vectors for candidate models, using benchmark detection statistics or supplier-declared capability profiles as initial values. The inherent capability boundary components of industry models and vertical models are significantly higher than those of lightweight general models in the dimensions of domain specialization and knowledge timeliness, while the inherent capability boundary components of lightweight general models are relatively lower in the dimensions of task complexity and inference chain length. During the execution phase, the tiered model matching module receives the requirement feature vector from the target semantic parsing module, enumerates each candidate model level one by one in the outer layer, calculates the residual requirements dimension by dimension, and passes the residual requirement vector to the capability assembly recommendation module to trigger the assembly of inner components; the outer layer then compares the comprehensive costs of each candidate model together with its inner component subsets and selects the optimal solution.

[0089] The capability assembly recommendation module is used to construct compensable capability vectors for each capability component of tool plugins, knowledge bases, and workflow templates on the same capability dimension, and to model the marginal compensation gain of the capability component in each capability dimension as a conditional quantity related to the residual capability of the selected model in that capability dimension, so that the marginal gain of the capability component in the strong dimension of the model is saturated and the marginal gain in the weak dimension of the model is increased; and to aggregate the inherent capability boundary components of the candidate model and the conditional quantities of the selected component subset in a saturated manner into a comprehensive supply capability, under the constraint that the coverage gap in each capability dimension does not exceed the preset tolerance, to solve the model selection and component combination that minimizes the sum of the capability redundancy weighted sum, the model cost, and the component cost, thus obtaining the capability assembly scheme. Specifically, the capability assembly recommendation module maintains a capability component library, where the compensable capability vectors of the knowledge base have higher weights in the dimensions of domain specialization and knowledge timeliness than in other dimensions; tool plugins have higher weights in the dimensions of real-time requirements and tool call density than in other dimensions; and workflow templates have higher weights in the dimensions of task complexity and inference chain length than in other dimensions. After receiving the residual demand vector, the module greedily selects a subset of components using a weighted set coverage method, based on the ratio of the marginal compensation gain to the cost of introduction for each component in the dimension with remaining gaps, until the residuals in each dimension are filled. The module outputs the capability assembly scheme and the comprehensive supply capability components for each dimension, and passes them to the agent instantiation module. Preferably, the capability assembly recommendation module also supports introducing a coupling matrix between capability dimensions in the aggregation of comprehensive supply capabilities to characterize the cross-dimensional compensation effect of knowledge compensation indirectly mitigating complex reasoning.

[0090] The agent instantiation module is used to present the capability assembly scheme and its coverage map for user confirmation or adjustment, thereby completing the agent instantiation creation. Specifically, the agent instantiation module receives the capability assembly scheme and comprehensive supply capability vector from the capability assembly recommendation module, generates a coverage map for each capability dimension (simultaneously indicating demand intensity, comprehensive supply capability, coverage gap, and capability redundancy using a radar chart or parallel bar chart), and presents it to the user through a visual interface; when the user adjusts the model level or capability components, the user constraints are fed back to the capability assembly recommendation module to trigger a re-solution under the user constraints, ensuring that the gaps in each dimension do not exceed the preset tolerance; after user confirmation, the agent instantiation creation is automatically completed according to the determined model selection, parameter configuration, and capability component combination, and a runnable agent instance is output.

[0091] The matching strategy optimization module is used to collect operational performance data of the created agents and iteratively update the extraction calibration parameters of the inherent capability boundary vector, the conditional quantities, and the demand feature vector. Specifically, the matching strategy optimization module continuously collects operational performance data of the instantiated agents in actual operation, such as task completion quality, actual capability performance of each capability dimension, response latency, and resource consumption. Based on the above data, the inherent capability boundary vector is exponentially corrected based on actual capability performance; the component conditional compensation gain is updated with regression based on actual improvement; and the demand extraction calibration parameters are updated with incremental feedback samples confirmed or adjusted by the user to update the anchor points and scale of the analytical model. After the correction is completed, the updated capability vector and calibration parameters are written back to the shared storage layer for use by each module in subsequent assembly, so that capability modeling is continuously iteratively optimized with the accumulation of operational feedback, and the accuracy of the exact coverage of subsequent agents is continuously improved.

[0092] The data flow between modules is as follows: the target semantic parsing module sends the requirement feature vector and confidence vector to the tiered model matching module; the tiered model matching module passes the residual requirement vectors of each level to the capability assembly recommendation module one by one; the capability assembly recommendation module uploads the capability assembly scheme and coverage data to the agent instantiation module; user adjustment commands are fed back from the agent instantiation module to the capability assembly recommendation module to trigger the constraint re-solution; the matching strategy optimization module continuously collects running data asynchronously and writes the corrected parameters back to the storage layer, and each module automatically uses the updated parameters in the next assembly.

[0093] Example 5: This example illustrates the implementation process and technical effects of the method described in Example 1 through a specific application scenario.

[0094] An operations department within a group company needs to create several enterprise intelligent agents for after-sales and quality feedback across multiple product lines. This scenario presents the following typical challenges: First, the business scenarios are wide-ranging, ranging from simple work order routing to multi-step quality issue tracing, with significant differences in task complexity and inference chain length requirements; second, quality feedback involves numerous industry-specific inspection procedures and terminology standards, demanding high levels of domain expertise and timely knowledge; third, work order routing requires real-time integration with internal enterprise tools, with clear requirements for real-time performance and tool call density; fourth, quality inspection data involves supply chain and customer information, making data sensitivity significant. Under the aforementioned multidimensional and heterogeneous requirements, if a conventional sequential decision-making approach is adopted—first selecting the model level based on experience, and then adding components one by one without considering the coupling relationship between the inherent capabilities of the model and the compensation of the components—two types of mismatches are likely to occur: If a lightweight general-purpose model is selected first, the inherent shortcomings in the domain-specific dimension must be compensated by adding a massive domain knowledge base, resulting in a significantly high cost of introducing components; if a vertical domain model is selected, the domain-specific capabilities have already been internalized, and if a general knowledge base is still conventionally provided, obvious capability redundancy will occur. Both of these mismatches stem from the lack of explicit modeling of the coupling relationship between the model and components in conventional methods. This invention is designed to solve this problem.

[0095] This embodiment addresses a typical business objective—"to build an after-sales intelligent agent that can automatically classify and prioritize work orders from multiple sources, and automatically generate quality inspection reports according to standard inspection procedures after processing"—by executing each step sequentially according to the method described in Embodiment 1.

[0096] Step S1 execution: The target semantic parsing module performs dimension-by-dimensional semantic extraction on the above business objective description, obtaining the following requirement feature vector: Task complexity 0.72, Domain specialization 0.68, Real-time requirements 0.55, Interaction mode 0.48, Inference chain length 0.65, Knowledge timeliness 0.60, Tool call density 0.62, and Data sensitivity 0.75. Among these, the high task complexity and inference chain length reflect the complex inference requirements for generating multi-step quality inspection reports; Data sensitivity is the highest, reflecting the sensitive attributes of supply chain and customer data. The confidence levels of all dimensions are higher than the preset threshold of 0.6, so no further tolerance is required for any dimension.

[0097] Execution of steps S2 and S3: The platform has three built-in candidate models, whose inherent capability boundary vectors (using domain expertise, task complexity, and tool call density as examples) are as follows: Lightweight general-purpose model (0.38 / 0.42 / 0.35), industry model (0.74 / 0.68 / 0.55), and vertical quality inspection model (0.89 / 0.72 / 0.60). In the capability component library, the compensable capability vector of the quality inspection knowledge base is approximately 0.35 in the domain expertise dimension and approximately 0.38 in the knowledge timeliness dimension; the work order routing plugin is approximately 0.45 in the real-time dimension and approximately 0.40 in the tool call density dimension; the multi-step workflow template is approximately 0.32 in the task complexity dimension and approximately 0.35 in the inference chain length dimension. The marginal compensation gain of each component is modeled using conditional quantities: for model tiers with strong capabilities, the actual gain of the component in this dimension tends to saturate; for tiers with weaker capabilities, the gain is more significant.

[0098] Step S4 execution: The outer layer enumerates the three candidate models one by one, and the inner layer greedily assembles the components according to the residual requirements of each level. The enumeration results are as follows: When using the lightweight general model, the domain expertise residual is 0.30, the tool call density residual is 0.27, and the inference chain length residual is 0.30. The inner layer needs to be overlaid with a quality inspection knowledge base, a work order routing plugin, and a workflow template to fill all the gaps, and the overall cost score is 0.81. When using the industry model, the domain expertise dimension has been internalized by the model (boundary 0.74 > requirement 0.68), and the inner layer does not need to equip a quality inspection knowledge base for this dimension. Conditional modeling causes the actual gain of the knowledge base in this dimension to automatically drop to near 0 due to saturation. The inner layer only needs to equip a work order routing plugin (to fill the tool call density gap of 0.07), and the inference chain length residual is less than 0.01 (negligible, within the tolerance range of τ_d=0.05), and the overall cost score is 0.57. When using the vertical quality inspection model, the model itself has a high cost, even though the component assembly is extremely simple, and the overall cost score is 0.73. The outer layer selects a combination of industry model and work order routing plugin as the capability assembly scheme. The comprehensive supply capacity of each dimension meets the constraint that the coverage gap does not exceed the preset tolerance τ_d=0.05, and the capability redundancy of each dimension is less than 0.10.

[0099] Step S5 execution: The system presents the coverage map of the selected solution to the user through a radar chart. The comprehensive supply capacity line and demand intensity line of the eight dimensions are highly consistent, and the redundancy of each dimension is within an acceptable range with no obvious gaps. No adjustments are required from the user. After confirmation, the intelligent agent instantiation is completed.

[0100] Technical Effect Verification: This embodiment uses three indicators—capability coverage completeness rate (the proportion of dimensions with coverage gaps not exceeding τ_d), capability redundancy rate (the average redundancy of each dimension), and comprehensive cost score—to compare and evaluate the following four schemes. The comprehensive cost score is obtained by normalizing and weighting the model cost and component cost separately: first, the cost vector of each candidate model (the weighted normalized value of inference latency and resource overhead) and the component cost are mapped to the [0,1] interval, respectively. The normalization benchmark for model cost is the highest model cost in the candidate model system, and the normalization benchmark for component cost is the upper bound of the sum of the costs of all candidate components. After normalization, a weighted sum is performed according to a preset weight coefficient, i.e., comprehensive cost score = normalized model cost + sum of normalized component costs. In this embodiment, the weight coefficient is 1.0. The capability redundancy rate is defined as the weighted average of redundancy on demand intensity for each capability dimension, where the weight is the demand intensity value of the corresponding dimension to highlight the redundancy cost of high-demand dimensions. Figure 6 As shown, a dimension-by-dimensional comparison of the demand intensity, the comprehensive supply capacity of this invention, and the comprehensive supply capacity of the serial baseline is made across eight capability dimensions. The comprehensive supply curve of this invention closely follows the demand curve in all dimensions with a redundancy mean of approximately 0.06. The serial baseline, on the other hand, significantly exceeds the demand curve in dimensions such as domain specialization and data sensitivity, with a redundancy mean of approximately 0.19.

[0101] Option 1 (This invention, coupled joint solution + conditional quantity modeling): Capability coverage completeness rate 100%, capability redundancy rate 0.06, comprehensive cost score 0.57.

[0102] Option 2 (serial baseline, first fix the industry model, then add all conventional components one by one, without considering coupling gain saturation): 100% capability coverage, 0.19 capability redundancy, and a comprehensive cost score of 0.74. This option, because it does not model coupling gain saturation and additionally adds a quality inspection knowledge base in the domain expertise dimension, results in significant capability redundancy, and its comprehensive cost score is 0.17 higher than this invention.

[0103] Option 3 (Ablation verification, removing conditional modeling, treating component gain as a fixed constant): For example... Figure 7 As shown ( Figure 7In this model, 'a' represents the saturation rate parameter α. The normalized marginal compensation gain of the component varies with the model residual capability following a family of saturation curves—the compensation gain tends to be sufficient when the model is weak in a certain dimension and tends to saturate when it is strong. Furthermore, the larger the saturation rate parameter α, the faster the decay. Removing this conditional variable from the model model is equivalent to reducing the above curves to constants and ignoring the modulation of the actual gain of the component by the model level. The capability coverage completeness is 92% (a slight gap of approximately 0.06 appears in the knowledge timeliness dimension, exceeding the tolerance), the capability redundancy rate is 0.08, and the overall cost score is 0.61. This ablation result indicates that conditional variable modeling is necessary for accurately predicting the actual compensation gain of the component at a specific model level. Its absence leads to deviations in the selection of inner-layer components to fill the actual gaps, and the coverage completeness of some dimensions fluctuates between 91% and 94% in specific batches, failing to consistently achieve complete coverage.

[0104] Option 4 (ablation validation, removing the two-layer coupled solution and replacing it with random sampling of model tiers based on budget thresholds followed by component selection according to fixed rules): Capability coverage completeness rate 78%, capability redundancy rate 0.22, and overall cost score 0.89. This comparison further demonstrates that the two-layer coupled solution is indispensable for simultaneously reducing redundancy and avoiding gaps; without coupled joint solution, both types of mismatches occur simultaneously.

[0105] The above four sets of comparative data show that, by coupling joint solution and conditional modeling, this invention reduces the capability redundancy rate from 0.19 to 0.06 and the overall cost score from 0.74 to 0.57 compared to the serial baseline scheme, while maintaining a capability coverage integrity rate of 100%. Compared to the ablation scheme that removes conditional modeling, the coverage integrity rate is improved from a fluctuating 91%~94% to a stable 100%. Compared to the ablation scheme that removes the two-layer coupled solution, the coverage integrity rate is improved by about 22 percentage points and the redundancy rate is reduced by about 0.16. The independent technical contributions of each innovation point are quantitatively verified.

[0106] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. 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 capability adaptive assembly and model matching method for enterprise intelligent agents, wherein the method is executed during the cold start assembly phase when the intelligent agent has no operational data, characterized in that, include: Step S1: Extract multi-dimensional semantics from the business objectives described by the user in natural language and map them into a demand feature vector on a unified capability-demand space. The unified capability-demand space is formed by capability dimensions including task complexity, domain expertise, real-time requirements and interaction modes. Step S2: For the general model, industry model and vertical model in the hierarchical candidate model system, construct an inherent capability boundary vector representing the upper bound of the capability that can be provided without external components on the same capability dimension, and construct a cost vector. Step S3: For each capability component of the tool plugin, knowledge base and workflow template, construct a compensable capability vector on the same capability dimension, and model the marginal compensation gain of the capability component in each capability dimension as a conditional quantity related to the residual capability of the selected model in that capability dimension, so that the marginal gain of the capability component in the strong dimension of the model is saturated and the marginal gain in the weak dimension of the model is increased. Step S4: Aggregate the inherent capability boundary components of the candidate model and the condition quantities of the selected component subset in a saturated manner to form a comprehensive supply capability. Under the constraint that the coverage gap of each capability dimension does not exceed the preset tolerance, solve the model selection and component combination that minimizes the sum of capability redundancy weighted sum, model cost, and component cost to obtain the capability assembly scheme. Step S5: Present the capability assembly scheme and its coverage map for user confirmation or adjustment, thereby completing the instantiation of the intelligent agent; Step S6: Collect the operational performance data of the created intelligent agent, and iteratively update the extraction and calibration parameters of the inherent capability boundary vector, the conditional quantity, and the demand feature vector.

2. The method according to claim 1, characterized in that, In step S1, each component of the demand feature vector is a demand intensity value that takes a value in the normalized interval on the corresponding capability dimension; step S1 also includes: using an analytical large model combined with a structured prompt template to extract the demand intensity of the business objective dimension by dimension, and calibrating the extraction results through preset anchor point examples; adding a confidence level to each demand intensity value, and the capability dimensions with a confidence level lower than the threshold are treated with a wider tolerance in the solution of step S4.

3. The method according to claim 1, characterized in that, In step S2, the inherent capability boundary vector is obtained by statistically analyzing the benchmark detection results of the candidate model in each capability dimension, or by initializing the capability profile declared by the model vendor and then correcting it through subsequent feedback; the inherent capability boundary components of the industry model and vertical model in the domain expertise dimension are higher than those of the general model, and the inherent capability boundary components of the general model in the task complexity dimension are lower than those of the industry model and vertical model; the cost vector includes inference latency and resource overhead.

4. The method according to claim 1, characterized in that, In step S3, the compensable capability vector of the knowledge base has a higher component in the dimensions of domain expertise and knowledge timeliness than in other dimensions; the compensable capability vector of the tool plugin has a higher component in the dimensions of real-time requirements and tool call density than in other dimensions; and the compensable capability vector of the workflow template has a higher component in the dimensions of task complexity and inference chain length than in other dimensions.

5. The method according to claim 1, characterized in that, The solution of step S4 is implemented in a two-layer structure: the outer layer enumerates candidate models one by one in the tiered model levels. For each enumerated candidate model, the residual requirement is obtained by subtracting the inherent capability boundary vector from the requirement feature vector dimension by dimension. The inner layer, for the residual requirement, greedily selects capability components in a weighted set coverage manner based on the ratio of the marginal compensation gain of the capability component in the dimension with the gap to its introduced cost, until the residual requirements in each dimension are filled, and obtains the component subset under that level. The outer layer then compares the comprehensive cost of each candidate model together with its inner component subset, and selects the model and component combination with no coverage gap and the lowest comprehensive cost as the capability assembly scheme.

6. The method according to claim 5, characterized in that, The inner layer solution is replaced by one of branch and bound, integer programming relaxation, or local search; when the number of model bits exceeds a preset value, the outer layer enumeration is replaced by pruning search in ascending order of cost.

7. The method according to claim 1, characterized in that, In step S5, the coverage map indicates the demand intensity, the comprehensive supply capacity, and the remaining coverage gaps and capacity redundancy in a capability-dimensional manner, and explicitly externalizes the redundancy and gaps into objects for user confirmation or adjustment; when the user adjusts the model level or adds or deletes capability components, step S4 is triggered to re-solve under the constraints imposed by the user, so that the capability assembly scheme keeps the coverage gaps of each capability dimension from exceeding the preset tolerance.

8. The method according to claim 1, characterized in that, In step S6, the operational performance data includes task completion quality, actual capability performance in each capability dimension, response latency, and resource consumption. After correcting the inherent capability boundary vector and the condition quantity based on the operational performance data, steps S1 to S5 are re-executed for the subsequently received business objectives.

9. The method according to claim 1, characterized in that, In step S3, the compensation of the capability component in the first capability dimension is modeled as changing the effective residual of the selected model in the second capability dimension. This introduces a coupling matrix between capability dimensions into the aggregation of the comprehensive supply capability, making the solution in step S4 a joint solution that is indivisible between capability dimensions. Furthermore, the elements of the coupling matrix are iteratively updated by collecting the operational effect data of the created agents in a gradient descent manner. When the number of historical deployment samples corresponding to a certain coupling coefficient is lower than a preset threshold, the coupling coefficient remains unchanged at its initial value, and data-driven updates are initiated only after the number of samples reaches the threshold.

10. A capability adaptive assembly and model matching system for enterprise intelligent agents, wherein the system operates during the cold start assembly phase when the intelligent agent has no operational data, characterized in that, include: The target semantic parsing module is used to extract multi-dimensional semantics from the business objectives described by users in natural language and map them into a demand feature vector on a unified capability-demand space. The unified capability-demand space is composed of capability dimensions including task complexity, domain expertise, real-time requirements, and interaction modes. The tiered model matching module is used to construct inherent capability boundary vectors representing the upper bound of capabilities that can be provided without external components for general models, industry models, and vertical models in the tiered candidate model system on the same capability dimension, and to construct cost vectors. The capability assembly recommendation module is used to construct compensable capability vectors for each capability component of tool plugins, knowledge bases, and workflow templates on the same capability dimension, and to model the marginal compensation gain of the capability component in each capability dimension as a conditional quantity related to the residual capability of the selected model in that capability dimension, so that the marginal gain of the capability component in the strong dimension of the model is saturated and the marginal gain in the weak dimension of the model is increased. It is also used to aggregate the inherent capability boundary components of the candidate model and the conditional quantity of the selected component subset in a saturated manner into a comprehensive supply capability. Under the constraint that the coverage gap in each capability dimension does not exceed the preset tolerance, it solves the model selection and component combination that minimizes the sum of capability redundancy weighted sum, model cost, and component cost, and obtains the capability assembly scheme. The agent instantiation module is used to present the capability assembly scheme and its coverage map for user confirmation or adjustment, thereby completing the agent instantiation creation. The matching strategy optimization module is used to collect the operational performance data of the created intelligent agents and iteratively update the extraction calibration parameters of the inherent capability boundary vector, the condition quantity, and the demand feature vector.

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