Development management system and method of API (Application Program Interface)

Abstract

The invention discloses a development management system and method for an API interface, and relates to the technical field of software engineering. The system comprises: an intention understanding and design engine, which analyzes a multi-modal service demand into a structured API development task; the agents collaboratively develop a network, and the multiple AI agents are dispatched to generate API specifications, codes, test cases and deployment configurations in parallel; the API ecological dynamic map construction module is used for automatically constructing and updating a dynamic map of dependency and data flow direction between APIs based on a generated product; a continuous learning and evolution engine analyzes the atlas and operation data to identify optimization points and generate or execute evolution policies. All the modules form a closed loop, and output of the evolution engine is fed back to the intention understanding engine to optimize subsequent design. The corresponding method comprises the steps of demand analysis, intelligent collaboration, atlas construction and closed-loop optimization. According to the method, automatic development, intelligent collaboration and continuous architecture optimization of the API are realized, and the development efficiency and the system maintainability are improved.

Description

Technical Field

[0001] This invention relates to the field of software engineering technology, and more specifically, to an API interface development management system and method. Background Technology

[0002] In current software engineering practices, especially in the era of microservices and cloud-native architectures, Application Programming Interfaces (APIs) have evolved from simple internal communication mechanisms into core carriers of enterprise digital assets and business capabilities. With increasing system complexity, the number of APIs has exploded, and their interaction networks have become increasingly complex, posing unprecedented challenges to traditional API development and management models. From initial design and collaborative development to later operation and architecture evolution, the entire lifecycle involves numerous stages relying heavily on manual labor, broken toolchains, and delayed information feedback, leading to increasingly prominent efficiency bottlenecks and quality risks.

[0003] Specifically, existing API development and management models suffer from several systemic problems. In terms of development efficiency, a significant gap exists between unstructured business requirements and executable API code. This process heavily relies on developers' manual translation, design, and coding, which is not only time-consuming and labor-intensive but also prone to errors due to misunderstandings. While some code generation tools or low-code platforms exist, they typically require strictly defined input formats (such as detailed configuration forms or specific model files) and cannot directly understand flexible requirements like natural language or sketches. Their level of intelligence is limited, failing to fundamentally liberate productivity. In terms of governance and control, there is insufficient visibility after API deployment. Traditional API gateways or registries primarily focus on traffic management and simple directory listing, lacking real-time, dynamic, and comprehensive depiction of deep API call dependencies, data flow, and architectural health. When an API needs to be changed, it is difficult to accurately and quickly assess its impact; change decisions are often based on incomplete information or experience, easily leading to online failures. In terms of continuous evolution capabilities, existing methods are primarily reactive and passive. Architectural optimization or defect fixing typically relies on post-event monitoring and alerts or periodic manual reviews, lacking the ability to proactively analyze, predict, and automate evolution based on runtime data and topology relationships. "Architectural decay" issues in the API ecosystem, such as interface redundancy, contract drift, and performance degradation, are difficult to detect and eradicate in a timely manner.

[0004] Therefore, the industry urgently needs an innovative, systematic solution that can bridge the gap between business intent and technical implementation, enabling automated and intelligent collaboration in the development process; build and maintain a real-time, accurate API ecosystem map to provide deep insights for governance; and ultimately form a closed loop that can continuously learn from operational feedback and drive autonomous optimization of the API architecture. This is not only an upgrade in tools to improve R&D efficiency, but also a key exploration for building a sustainable and adaptive architectural paradigm for complex software systems in the future. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an API interface development management system and method to solve the problems mentioned in the background art.

[0006] In a first aspect, embodiments of this application provide an API interface development management system and method, including: Intent Understanding and Design Engine: Used to receive multimodal input and use artificial intelligence technology to parse and decompose unstructured business requirements into structured API development tasks; Intelligent Agent Collaborative Development Network: Used to schedule multiple dedicated AI agents to generate corresponding API development products in parallel according to the structured API development tasks. The API development products include API specifications, code implementation, test cases, and deployment configuration. API Ecosystem Dynamic Graph Construction Module: Based on the API development artifacts, automatically construct and update a dynamic graph, which is used to reflect the dependencies between APIs, data flow, and call relationships; Continuous learning and evolution engine: Analyzes the dynamic graph and runtime data to identify optimization points and generate secure API evolution strategies; The system forms a closed loop: the output of the continuous learning and evolution engine is fed back to the intent understanding and design engine to optimize its subsequent task decomposition and design decisions.

[0007] In some embodiments of this application, multimodal input includes at least one of natural language description, business process diagram, interface sketch, existing API documentation, or code.

[0008] In some embodiments of this application, the dedicated AI agent includes at least: Design an intelligent agent to generate interface definition files that conform to the OpenAPI specification based on the structured API development task; Develop intelligent agents to generate server-side framework code, client-side SDK, or business logic stub code based on the interface definition file; The test agent is used to generate interface test cases, performance test scripts, or security test plans based on the interface definition file. An operations and maintenance intelligent agent is used to generate containerized deployment description files and monitoring and alarm configurations related to the API.

[0009] In some embodiments of this application, the API ecosystem dynamic graph construction module is specifically used for: By analyzing the API's code repository, configuration files, and online traffic logs, the system automatically discovers the call dependencies between services. Present the API's service topology, data flow path, and real-time health status using a visual graph structure; When any API plan is changed, the impact scope analysis is performed based on the dynamic graph, and all affected downstream services and consumers are identified.

[0010] In some embodiments of this application, the continuous learning and evolution engine is specifically used for: Collect runtime performance metrics, error logs, and architectural metrics data for the API; The data is analyzed using machine learning models to identify performance bottlenecks, architectural quirks, or outdated interfaces. Based on the analysis results, optimization suggestions such as interface splitting, caching strategy introduction, version iteration or code refactoring are automatically generated, and some low-risk evolution operations are executed after authorization.

[0011] Secondly, embodiments of this application provide a method for developing and managing API interfaces, including the following steps: S1: Receive multimodal business requirement inputs, parse them and decompose them into structured API development tasks through intent understanding technology; S2: Based on the structured API development task, schedule multiple AI agents to work collaboratively and generate corresponding API specifications, executable code, test cases and deployment configurations in parallel; S3: Automatically builds and continuously updates dynamic maps based on data generated by the API at runtime; S4: Analyze the dynamic graph and running data, identify optimization points and generate evolution strategies, and feed the evolution strategies back to the intent understanding process in step S1 to form a closed-loop optimization.

[0012] In some embodiments of this application, parsing and decomposition specifically includes: using a natural language processing model to extract entities, operations, and business rules from the requirements description, matching them with a historical case library, and outputting a design scheme that includes API style, data model, and atomic task list.

[0013] In some embodiments of this application, the collaborative work of AI agents specifically includes: designing agents to generate OpenAPI specifications; developing agents to synchronously generate multi-language server and client code; testing agents to generate test cases according to the specifications; and operating agents to generate infrastructure, i.e., code configuration.

[0014] In some embodiments of this application, constructing a dynamic graph specifically includes: automatically drawing a service dependency graph by parsing call chain data, network traffic, and deployment descriptions; and associating the performance status, version information, and contract definitions of APIs in real time within the graph.

[0015] In some embodiments of this application, identifying optimization points and the closed-loop optimization specifically include: analyzing interface call popularity and performance bottlenecks based on graph analysis, recommending interface splitting or merging; detecting and warning of incompatible change risks; and storing confirmed effective evolution patterns in a case library for optimizing the initial design of subsequent similar requirements.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. Existing technologies are mostly independent tools that assist in single-point processes (such as generating code from Swagger documentation), with each step being fragmented and relying on manual coordination. This invention directly parses unstructured requirements such as natural language through an "intent understanding and design engine," and utilizes an "intelligent agent collaborative development network" to schedule multiple dedicated AI agents to generate specifications, code, tests, and deployment configurations in parallel. This not only liberates developers from repetitive translation and coding work, but more importantly, ensures that the design, development, and testing stages are carried out synchronously based on the same machine-readable "structured task," fundamentally eliminating deviations caused by information asynchrony and achieving high-quality, highly consistent automated pipeline operations.

[0017] 2. Traditional API management relies on static registry centers or documentation, which cannot reflect the actual call topology and health status at runtime. This invention, through the "API Ecosystem Dynamic Graph Construction Module," proactively analyzes code, configuration, and online traffic to automatically build and continuously update a dynamic graph reflecting API dependencies and data flow. This graph provides unprecedented visualization insights for architecture governance, enabling the analysis of the impact of any API change, fault tracing, and architecture compliance checks to be transformed from time-consuming manual analysis into precise calculations and visualizations within seconds, greatly improving the controllability of system complexity and the accuracy of operational decisions.

[0018] 3. Existing methods for API optimization are mostly reactive and reactive, acting as remedial measures after the fact. This invention's "Continuous Learning and Evolution Engine" proactively identifies performance bottlenecks, architectural smells, and outdated interfaces by analyzing dynamic graphs and runtime performance data, and automatically generates or executes optimization strategies after authorization. Crucially, its output can be fed back to the front-end intent understanding engine, embedding validated optimization patterns into system knowledge for use in optimizing the initial design of similar future requirements. This transforms the system beyond a mere tool into an "organism" capable of learning from history and operation, continuously iterating and evolving, effectively delaying architectural decay and ensuring long-term controllable technical debt and architectural health. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0020] Figure 1 A system composition diagram provided for this invention; Figure 2 A flowchart of the method provided by the present invention; Figure 3 Detailed diagram of the intelligent agent network provided by this invention; Figure 4 Application diagram for constructing the spectrum provided by this invention; Figure 5 The evolution closed-loop working diagram provided by the present invention. Detailed Implementation

[0021] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] Example 1 Please see Figure 1This invention provides an API interface development and management system. To achieve the above objectives, this invention is implemented through the following technical solution: An API interface development and management system, characterized by comprising: an intent understanding and design engine, an intelligent agent collaborative development network, an API ecosystem dynamic graph construction module, and a continuous learning and evolution engine. These four core modules communicate through carefully designed interface protocols and data buses, forming a complete, closed-loop intelligent software engineering lifecycle support platform from requirement perception to architectural autonomy. The system is deployed in a Kubernetes-based cloud-native environment. Each module is encapsulated as a microservice, possessing independent elastic scaling capabilities, and performs service discovery, traffic governance, and secure communication through a unified service mesh. The system's core data flow begins with the intent understanding and design engine "digesting" the original requirements and ends with the continuous learning and evolution engine "feeding back" optimized knowledge to the design starting point in a feedback loop, realizing the core capability of continuous self-improvement and self-evolution during system operation.

[0024] Specifically, the intent understanding and design engine, acting as the "brain cortex" and "requirement translator" of the entire system, is physically deployed with a highly available API gateway cluster, a distributed task queue, and a set of inference services supporting AI models. Its technical architecture is divided into three layers: the access layer, the understanding layer, and the design layer. The access layer supports diverse input protocols, including but not limited to HTTP API calls (receiving natural language in JSON format), WebSocket connections (for real-time interactive requirement clarification), file upload endpoints (receiving flowchart PNG / JPG files and Markdown documents), and Webhook integration with Git repositories (automatically listening for requirement document submissions). The understanding layer is the core, integrating a multimodal fusion understanding model. For natural language text, a pre-trained large language model fine-tuned from a large-scale software engineering corpus (such as GitHub Issues, API documents, and technical specifications) is used. This model not only performs general intent recognition and entity extraction but also understands specific concepts in areas such as "interface," "authentication," "pagination," "idempotency," and "degradation." For image input (such as flowcharts and sketches), a vision-language joint model is used. First, a convolutional neural network extracts graphic features, identifying elements such as boxes, arrows, and text labels, along with their topological relationships. This is then converted into structured text descriptions and fed into a language model for deep understanding. The design layer includes a rule engine and a pattern matching library. The rule engine incorporates company-level API design specifications (such as naming conventions, version management strategies, and security standards) to perform compliance checks and automatic corrections on the initial design drafts output from the understanding layer. The pattern matching library stores hundreds or thousands of abstracted, reusable API design patterns (such as "pagination query pattern," "file upload pattern," and "OAuth2.0 client credential flow pattern"). The system matches the current requirements with patterns in the library based on similarity, automatically recommending or adapting the optimal design solution to generate the final, high-quality "structured API development task." This task is a machine-readable blueprint containing complete semantics, whose data structure is defined as a comprehensive JSON object containing id, name, description, business_logic, functional_requirements (subdivided into a list of endpoints, each endpoint containing method, path, request, response, auth, etc.), non_functional_requirements (such as qps, latency, availability), data_models (defining all involved DTOs), design_pattern_references, and compliance_constraints.

[0025] In this embodiment, a specific API group requirement for "e-commerce coupon issuance and redemption" is used as an example to illustrate the workflow of the intent understanding and design engine in detail: The product manager inputs a natural language description on the interactive interface provided by the system: "We need a set of APIs to manage coupons. The functions include: 1. The operations backend can create new coupons and set the type (full reduction, discount), face value, validity period, applicable product range, and inventory. 2. Users can claim coupons in the APP and view their available coupon list. 3. When placing an order, the system can redeem coupons and deduct inventory." At the same time, a simple coupon status flow sketch is uploaded. The access layer packages the text and image into a multimodal request. The understanding layer starts: the text part is segmented and vectorized and then input into the fine-tuned LLM. The model identifies the core entity: "Coupon", whose attributes include "type", "face value", "validity period", "applicable range", and "inventory"; identifies the operation roles: "Admin" and "User"; and identifies the core operations: "Create", "Acquire", "List", and "Consume". The image portion is parsed, identifying state flows such as "Creation - Approval - Activation" and "User Claim - Held - Order Redemption - Used". After multimodal information fusion, a preliminary semantic network is formed. The design layer takes over: the rule engine automatically divides the interface into two path prefixes, / admin / api / v1 / coupons and / client / api / v1 / coupons, based on the different roles of "Operations Backend" and "User APP", and applies the rule "must be in UUID format" to all path parameters involving IDs. The pattern matching library is triggered, identifying patterns such as "Create Resource", "Paginated List Query", and "Status Change", and automatically adding query parameters such as page, size, and status to the GET / client / api / v1 / coupons endpoint.Ultimately, the engine outputs a structured API development task. The `functional_requirements.endpoints` field details endpoints such as `POST / admin / api / v1 / coupons` (create), `GET / admin / api / v1 / coupons / {id}` (query details), `PUT / admin / api / v1 / coupons / {id} / stock` (adjust inventory), `POST / client / api / v1 / coupons / {code} / acquire` (acquire), `GET / client / api / v1 / coupons?page=1&size=10` (my coupon list), and `POST / client / api / v1 / coupons / {code} / consume` (redeem). Each endpoint has a predefined request / response body structure, possible HTTP status codes, and a role-based access control array. This structured task becomes the sole authoritative source driving all subsequent automated steps.

[0026] Example 2 Please see Figure 2 This invention provides a method for developing and managing API interfaces. To achieve the above objectives, this invention is implemented through the following technical solution: A method for developing and managing API interfaces, characterized by comprising steps S1 to S4, which constitute a highly automated, parallel, and perceptual feedback-enabled software delivery pipeline. This method reflects the dynamic behavior of the system, and its execution is orchestrated and driven by an internal workflow engine (such as Camunda). Each step is a measurable and rollbackable atomic operation, and data transfer between steps is completed through a shared, versioned context storage (such as Redis or a database), ensuring the reliability and observability of the entire process.

[0027] Specifically: Step S1 receives multimodal business requirement input and parses and decomposes it into structured API development tasks using intent understanding technology. This step not only completes the conversion from natural language to machine language, but more importantly, it completes the first accurate mapping from "business domain" to "technical domain". Step S2, based on the structured API development tasks, schedules multiple AI agents to work collaboratively, generating corresponding API specifications, executable code, test cases, and deployment configurations in parallel. This is the core innovation of this method, breaking the traditional waterfall or single-threaded agile iterative model of "requirements-design-coding-testing-deployment" in software engineering, and creatively introducing a parallel collaborative paradigm based on "intelligent agents". The system's built-in intelligent agent scheduler is essentially a distributed task distribution and coordination system. After receiving the structured tasks produced in S1, it immediately performs task graph analysis, identifies all subtasks that can be executed in parallel (such as generating OpenAPI specifications, generating Java server-side code, generating TypeScript client-side code, generating integration test cases, and generating K8sYAML), and calculates dependencies (e.g., the generated code depends on the existence of the specification). Subsequently, the scheduler asynchronously assigns these subtasks to the corresponding dedicated AI agents via message queues based on the load and capability tags of each agent. All agents start working simultaneously, reading the same structured task from a shared context as their input source, ensuring consistency in the working foundation. Intermediate outputs between them (such as the OpenAPI specifications generated by the design agent) are written back to the shared context in real time for consumption by other dependent agents. This parallelization compresses the traditional manual sequential work that would take hours or even days into a few minutes. Step S3, based on the data generated by the API during runtime, automatically builds and continuously updates a dynamic graph reflecting API call dependencies and data flow. This step expands the system's perspective from "development time" to "runtime," linking the static design blueprint with dynamic system behavior through continuous monitoring, forming a digital twin. Step S4, analyzes the dynamic graph and runtime data, identifies optimization points and generates evolution strategies, feeding the strategy knowledge back to the intent understanding process in step S1 to form a closed-loop optimization. This step endows the system with the ability to "think" and "evolve," making every system operation and every API call a source of nourishment for optimizing the system's future design.

[0028] In this embodiment, following the coupon API task of Embodiment 1, the collaborative execution process of step S2 is detailed: After receiving the task, the agent scheduler parses its functional_requirements and immediately creates six parallel jobs: Job1 (generates OpenAPI 3.0 specification) is assigned to the design agent; Job2 (generates SpringBoot server-side code) is assigned to the Java development agent; Job3 (generates ReactHooks client SDK) is assigned to the front-end development agent; Job4 (generates Postman collections and JMeter performance test scripts) is assigned to the testing agent; Job5 (generates K8s deployment manifest and HelmChart) is assigned to the operations agent; and Job6 (generates API usage documentation) is assigned to the documentation agent. The design agent completes Job1 first, filling the endpoints, data_models, and other information from the structured task into a predefined OpenAPI template, generating a complete coupon-service-openapi.yaml file, storing it in the shared context, and publishing a "specification ready" event. The Java development agent listens for this event, retrieves the YAML file, and uses the OpenAPIGenerator tool as its core to automatically generate a complete skeleton of the Spring Boot project, including the Controller, Service interface, DTO, Repository interface (assuming JPA is used), and basic configurations in application.yml. Simultaneously, based on the brief description in the business_logic field, it generates business logic stub methods with / / TODO annotations in the Service implementation class. The front-end development agent also listens for the "Specification Ready" event, generating the corresponding TypeScript type definition file and ReactHooks encapsulated with Axios. The testing agent, based on the OpenAPI specification, automatically generates a Postman test suite covering positive use cases (e.g., successful creation), boundary use cases (e.g., claiming when inventory is 0), and abnormal use cases (e.g., redeeming expired coupons), and generates a JMeter load test template simulating 100 redemption requests per second. The operations agent generates Kubernetes resource files such as Deployment, Service, and ConfigMap, and configures CPU / Memory monitoring based on Prometheus and a liveness probe based on the OpenAPI specification. After completing their tasks, all agents submit their output to a unified Git repository branch associated with the task ID. Once the scheduler detects that all jobs have been completed, it automatically triggers a continuous integration pipeline on that branch to execute unit tests and static code scanning.At this point, an API service codebase with a complete and operational form, along with its supporting assets, was ready for deployment and launch in a very short time.

[0029] Example 3 Please see Figure 3 This invention provides a specific implementation architecture and internal working mechanism of an intelligent agent collaborative development network in an API interface development and management system. To achieve the above objectives, this invention is implemented through the following technical solution: The intelligent agent collaborative development network is a loosely coupled, highly cohesive intelligent agent ecosystem. Each intelligent agent is an independent, containerized microservice, following a unified "intelligent agent contract" interface. This contract defines the intelligent agent's lifecycle management, task subscription and execution, context read / write, capability declaration, and health check standards. The core of the network is an intelligent agent registry and an event-driven communication bus.

[0030] Specifically, the dedicated AI agents in the network include, but are not limited to, design agents, development agents, testing agents, and operation and maintenance agents. Each agent's internal architecture includes: 1. A communication adapter: responsible for connecting to the event bus, subscribing to events of interest in specific task types, and publishing execution result events. 2. A context access layer: providing a unified API to read input data (structured tasks) from a shared context and write output artifacts. 3. A core capability engine: the "brain" of the agent, encapsulating the AI ​​model, code generation templates, rule base, or external tool call chains that implement its specific functions. 4. A quality and compliance checker: performing self-checks on intermediate results before outputting the final artifact (such as code style checks and specification compliance checks). For example, the development agent's core capability engine may integrate multiple code generators (such as template-based and AI model-based generators), code formatting tools, and syntax checkers. When it receives a task event to generate "Spring Boot server-side code", its internal workflow is as follows: First, it retrieves the OpenAPI specification file from the context; second, it selects the corresponding code generation template based on project metadata (such as groupId, artifactId); then, it calls OpenAPIGeneratorCLI or the corresponding library to generate basic code; next, it may call a finely tuned code model to intelligently populate the generated stub methods according to the business_logic description in the structured task, generating more semantic business logic code snippets; finally, it uses Checkstyle and SpotBugs to perform static analysis on the generated code to ensure there are no low-level errors, and then submits the final code to the context and publishes the event.

[0031] This embodiment takes the "Test Agent" as an example to deeply analyze its complex working mechanism. The Test Agent internally pre-sets various test strategy templates, such as "CRUD Interface Test Strategy," "Idempotency Test Strategy," and "High Concurrency Consistency Test Strategy." When it receives the task of generating test assets: First, contract parsing: It deeply parses the OpenAPI specification, understanding all possible inputs (including request body, path parameters, query parameters, header information) and their constraints (type, range, required fields) for each endpoint, as well as all possible outputs (successful response, various error codes). Second, test case generation: Applying software testing theories such as combinatorial testing or equivalence class partitioning, it automatically generates a minimal and complete test input dataset. For example, for the "Create Coupon" interface, it generates: valid data test cases (all fields are correct), boundary value test cases (validity period set to today, inventory 1), invalid data test cases (negative face value, type is a non-existent enumeration value), and test cases for missing required fields. Third, test script synthesis: It instantiates the generated test cases into scripts for a specific test framework. For API testing, it might generate RestAssured-based Java test classes, Pytest-based Python scripts, or a complete PostmanCollectionJSON file. Each test case is associated with the expected HTTP status code and response body verification. The fourth step is security and performance test scenario creation: it automatically identifies endpoints involving authentication and generates test cases carrying invalid or expired tokens. For POST and PUT operations, it checks their idempotency claims and automatically generates tests for duplicate request submissions. Furthermore, it generates basic JMeter or Gatling performance test scenario configurations based on the interface's QPS requirements. The fifth step is test data preparation and cleanup: the agent can even generate SQL scripts for preparing the test database state or calling other API preparation scripts, as well as post-test data cleanup scripts, ensuring test independence and repeatability. All these generated assets are structured and stored in a version control system along with the source code, forming a readily executable automated test suite strictly synchronized with the API contract.

[0032] Example 4 Please see Figure 4This invention provides a specific technical solution for data fusion, real-time computation, and visualization of an API ecosystem dynamic graph construction module within an API interface development and management system. To achieve the above objectives, this invention utilizes the following technical solution: The API ecosystem dynamic graph construction module is a complex network analysis system that integrates multiple data sources and performs near real-time computation. Its technology stack typically includes a log collector, a stream processing engine, a graph computation engine, and a visualization front-end. The module extracts information from scattered data silos, cleans, correlates, and enhances it through an ETL pipeline, and ultimately constructs and maintains a "live" graph with rich attributes and multi-dimensional correlations in a graph database (such as Neo4j or JanusGraph).

[0033] Specifically, this module continuously ingests and processes four main types of data sources: 1. Static parsing data: Regularly scans the source code repositories of all microservice projects, extracting API endpoint definitions, class dependencies, database table relationships, etc., through static analysis tools. 2. Configuration and deployment data: Obtains service runtime configurations, instance deployment locations, service discovery information, load balancing strategies, etc., from configuration centers, Kubernetes API Servers, and service registries. 3. Runtime tracing data: By integrating distributed tracing systems such as OpenTelemetry and SkyWalking, it non-intrusively collects detailed link data of all service calls, including caller, callee, protocol, interface, execution time, status code, etc. This is the primary source of the "dynamic" characteristic of the graph. 4. Business and performance metrics data: Obtains metrics such as API call count, response time, error rate, and throughput from monitoring systems. The core processing pipeline of the module is as follows: First, the data acquisition layer uses proxies such as Filebeat and Prometheusexporters to collect raw data from various sources. Secondly, the stream processing layer uses Apache Flink or Apache Kafka Streams to process tracking and metric data in real time, performing aggregation, correlation (associating tracking IDs with business metrics), and anomaly detection. Next, the graph modeling and storage layer transforms the processed data into CRUD operations on "vertices" and "edges" according to a predefined graph data model, and submits it to the graph database. The graph model is defined as follows: vertex types include Service, API Endpoint, Database, MessageQueue, etc.; edge types include CALLS (service A calls an API of service B), DEPENDS_ON (code-level dependencies), HOSTS_ON (services are deployed on a host or Pod), BELONGS_TO (API endpoints belong to a service), etc. Vertices and edges can be loaded with rich attributes, such as API version, method, and path, and edge avg_latency, error_rate, and call_frequency. Finally, the query and visualization layer provides GraphQL or REST APIs for other modules to query the graph and offers a powerful web visualization interface, allowing users to interactively explore service dependencies, drill down to view API details, and perform impact analysis.

[0034] This embodiment demonstrates how the module performs a real-time "impact scope analysis." Assume that operations personnel plan to make an incompatible change to the request body structure of the "Payment Service" POST / api / v1 / payments interface (e.g., changing the amount field from an integer to a floating-point number). In the graph visualization interface, the user simply clicks on the API endpoint node and selects "Analyze Upstream Impact." The system immediately initiates a reverse traversal query to the graph database: starting from the target APIEndpoint vertex, it searches along the CALLS edge in reverse, finding all other Service or APIEndpoint vertices that directly or indirectly call it. The query results are returned in milliseconds and a dependency subtree is highlighted in red on the interface. Simultaneously, the system provides a detailed report: 1. Direct Impact: Lists all client services that directly call this interface (e.g., "Order Service," "Marketing Service"), showing their call frequency and recent error rate. 2. Indirect Impact: Lists deeper services that may be indirectly dependent on through intermediate services. 3. Related Assets: Lists the code repositories, responsible parties, and most recent release dates corresponding to these affected services. Furthermore, the system can combine code analysis to pinpoint the specific lines of code calling the payment interface within the source code of affected client services, providing developers with precise modification guidance. This dynamic graph-based analysis transforms the complex dependency analysis work, which traditionally requires days of manual work and is still prone to omissions, into precise insights obtainable in seconds, greatly reducing change risks and collaboration costs.

[0035] Example 5 Please see Figure 5 This invention provides a complete implementation detail of the machine learning-driven analysis, intelligent decision-making, and automated operation loop of a continuous learning and evolution engine in an API interface development and management system. To achieve the above objectives, this invention is implemented through the following technical solution: The continuous learning and evolution engine is the key to the system's transition from "automation" to "autonomy." Essentially, it is an intelligent control loop integrating monitoring, analysis, planning, execution, and learning functions. The engine contains multiple parallel analysis pipelines, each focusing on a specific type of problem (such as performance, security, architecture, cost), and employing rules, statistical models, or machine learning models for detection. The engine's decision output is not a simple alarm, but an actionable, prioritized, and confidence-based "evolutionary strategy," which, upon approval, can drive downstream systems to automatically perform partial repair or optimization operations.

[0036] Specifically, the engine's workflow can be divided into four phases: perception, analysis, decision-making, execution, and learning. In the perception phase, the engine collects data extensively from dynamic graphs, APM systems, log platforms, cost management platforms, and more. In the analysis phase, multiple analyzers work in parallel: the performance analyzer uses time-series prediction models to detect trends in response time degradation and uses root cause analysis to pinpoint bottlenecks in the database, external APIs, or the engine's own code. The architecture analyzer uses community detection algorithms to identify modules with excessively high cohesion or tight coupling (service mesh dependencies) in the dynamic graph, recommending service splitting or merging. The security and compliance analyzer periodically scans dependency libraries with the latest CVE database and checks whether APIs violate new data privacy regulations (such as GDPR). The cost analyzer correlates API call volume with cloud resource consumption to identify inefficient resource configurations. In the decision-making phase, the engine summarizes the analysis results, resolves conflicts, and prioritizes them. It can generate various types of evolution strategies: code-level strategies (e.g., "It is recommended to add a Redis cache to the getUserInfo method, which is expected to improve response speed by 70%), configuration-level strategies (e.g., "The memory request value of the payment-service Pod is set too low, it is recommended to adjust it from 512Mi to 1Gi"), architecture-level strategies (e.g., "The call chain between order-service and inventory-service is too long, it is recommended to merge them into trade-service to simplify the architecture"), and process-level strategies (e.g., "The deployment frequency of coupon-service is below average, it is recommended to check its CI / CD pipeline"). Each strategy is accompanied by detailed analysis, estimated benefits, implementation complexity, and risk level. During the execution and learning phase, high-risk or high-complexity strategies are recommended for manual approval. Low-risk, highly patterned strategies (e.g., updating a dependency library version or adjusting a configuration parameter) can be executed directly by the engine within the "automatic repair window". Execution is usually accomplished by calling the system's CI / CD pipeline or infrastructure-as-a-code tools. Regardless of whether the strategy is executed automatically or implemented manually, its final effect (e.g., performance improvement metrics, error rate reduction) will be continuously tracked. These policy-effect pairs will be used as new training data and fed back to the machine learning model in the analysis phase to optimize its future detection accuracy and policy generation quality, thus achieving closed-loop learning.

[0037] In this embodiment, a complete "performance optimization and architecture evolution" case is used to illustrate the engine's operation: The continuous learning and evolution engine, through a performance analyzer, discovered that the P95 latency of the "order query" interface GET / api / v1 / orders / {id} had slowly increased from 50ms to 200ms over the past week. Root cause analysis pointed to the fact that this interface internally requires calls to four downstream interfaces: "user service," "product service," "coupon service," and "inventory service" to assemble data. Simultaneously, the architecture analyzer discovered from the dynamic graph that these four calls are executed sequentially, and that there is a dense network of calls between the "order service" and these four services. The engine synthesized this information and generated a high-priority architecture evolution strategy: "A performance bottleneck and architectural smell have been detected in the order details query interface. Recommendation 1 (short-term optimization): Transform the order details query into an asynchronous parallel call to downstream services and use Caffeine to implement local caching, which is expected to reduce latency to 80ms. Recommendation 2 (long-term refactoring): Introduce an 'order read model'. When an order is created / updated, synchronize the required data to a dedicated Elasticsearch index for querying through domain events, transforming complex queries into single queries to ES, which is expected to reduce latency to 20ms and completely decouple the queries." The strategy was pushed to the development team. After evaluation, the team decided to implement Recommendation 1 first. They clicked "Approve and Auto-implement Recommendation 1" on the system interface. The system then automatically performed the following operations: 1. Created a feature branch for the order service in the code repository. 2. Called the "Development Agent" and automatically rewrote the business logic code for the order details query based on the "Parallel Call and Aggregation Pattern" in the pattern library, changing the sequential call to a parallel call using CompletableFuture and adding a caching annotation. 3. Automatically modified the configuration and added a cache expiration time. 4. Submit code to trigger the CI / CD pipeline and run the complete automated test suite. 5. After the tests pass, the changes are automatically merged into the development branch and deployed to the pre-release environment for verification. Two weeks later, the engine confirmed that the P95 latency of the interface was stable at 75ms, demonstrating significant optimization. This successful "parallelization + caching" pattern was stored as a new case study in the historical pattern library. In the future, when the intent understanding and design engine encounters query requirements that need to aggregate multiple pieces of information, it can directly recommend this parallel caching design from the pattern library, instead of generating a traditional sequential call design. This achieves the positive flow and solidification of knowledge throughout the system lifecycle, truly reflecting the system's intelligent evolution capabilities.

[0038] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An API interface development and management system, characterized in that, include: Intent Understanding and Design Engine: Used to receive multimodal input and use artificial intelligence technology to parse unstructured business requirements into multiple candidate solutions. Each solution includes API specifications, data models, technology stack suggestions, and expected metrics. API solution generation and evaluation module: used to perform multi-level evaluation of the multiple candidate solutions, including performance simulation, security scanning, architecture compliance check, cost estimation, and select the optimal solution based on weighted scores and historical feedback data; Intelligent agent collaborative development network: used to schedule multiple dedicated AI agents to generate corresponding API development products in parallel according to the optimal solution; API Ecosystem Dynamic Graph Construction Module: Automatically constructs and updates the dynamic graph based on the API development artifacts; Continuous learning and evolution engine: Analyzes the dynamic graph and runtime data to identify optimization points and generate evolution strategies; The system forms a closed loop: the output of the continuous learning and evolution engine is fed back to the API solution generation and evaluation module to optimize its evaluation model and generation strategy.

2. The API interface development and management system according to claim 1, characterized in that, The multimodal input includes at least one of natural language description, business process diagram, interface sketch, existing API documentation or code; and the intent understanding and design engine adopts a multimodal fusion understanding model to convert inputs of different formats into a unified semantic representation.

3. The API interface development and management system according to claim 1, characterized in that, The API scheme generation and evaluation module includes: The multi-solution generation unit generates multiple candidate API design schemes based on the requirements analysis results. Each scheme includes different API architecture patterns, technology stack combinations, and deployment configurations. The multi-level evaluation unit performs performance simulation tests, security vulnerability scans, architecture compliance checks, development cost estimates, and team technical compatibility analysis for each candidate solution. The comprehensive scoring and selection unit generates a comprehensive score for each candidate solution based on a preset weighted scoring algorithm and combined with historical implementation effect data, and selects the optimal solution based on the scoring results. The solution explanation and recommendation unit generates a technical decision-making basis report for the selected optimal solution, including the solution's advantages, risk assessment, and implementation recommendations.

4. The API interface development and management system according to claim 1, characterized in that, The multi-level assessment includes at least one of the following assessment dimensions: Performance evaluation dimensions include API response time, throughput, and resource consumption under different load scenarios. Security assessment dimensions include static code analysis, dependency vulnerability scanning, and API attack surface detection. The architecture evaluation dimensions include assessing the rationality of the microservice decomposition, the degree of coupling between services, and scalability. From a cost assessment perspective, estimate the computing resources, storage resources, and third-party service costs required for the solution; From a compliance assessment perspective, the solution is checked to see if it complies with the organization's internal API design specifications, industry standards, and data privacy regulations.

5. The API interface development and management system according to claim 1, characterized in that, The dedicated AI agent includes at least: Design an intelligent agent to generate an interface definition file that conforms to the OpenAPI specification based on the optimal solution; Develop intelligent agents to generate server-side framework code, client-side SDK, or business logic stub code based on the interface definition file; The test agent is used to generate interface test cases, performance test scripts, or security test plans based on the interface definition file. An operations and maintenance intelligent agent is used to generate containerized deployment description files and monitoring and alarm configurations related to the API. Document intelligence is used to automatically generate API usage instructions, developer guides, and change logs.

6. The API interface development and management system according to claim 1, characterized in that, The API ecosystem dynamic graph construction module is specifically used for: By analyzing the API's code repository, configuration files, and online traffic logs, the system automatically discovers the call dependencies between services. Present the API's service topology, data flow path, and real-time health status using a visual graph structure; When any API plan is changed, the impact scope analysis is performed based on the dynamic graph, and all affected downstream services and consumers are identified; It provides a graph query interface that supports real-time retrieval and analysis based on service name, interface path, and data flow direction.

7. A method for developing and managing API interfaces, characterized in that, Includes the following steps: S1: Receive multimodal business requirement input and parse it into multiple candidate API design schemes through intent understanding technology; S2: Perform multi-level evaluation and scoring on each candidate solution, and select the optimal API design solution based on the scoring results; S3: Based on the optimal solution, schedule multiple AI agents to work collaboratively and generate corresponding API specifications, code, test cases and deployment configurations in parallel; S4: Automatically builds and continuously updates dynamic graphs based on data generated by the API at runtime; S5: Analyze the dynamic graph and running data, identify optimization points and generate evolution strategies, and feed the strategies back to the evaluation model in S2 to form a closed-loop optimization.

8. The API interface development and management method according to claim 7, characterized in that, The API design schemes that are parsed into multiple candidate schemes specifically include: Utilize natural language processing models to extract entities, operations, business rules, and non-functional requirements from the requirements description; By combining historical case libraries and API design pattern libraries, multiple candidate solutions with differences in architectural style, technical implementation, and deployment methods are generated; Each candidate solution includes detailed API endpoint definitions, data model design, technology stack recommendations, and expected performance metrics.

9. The API interface development and management method according to claim 7, characterized in that, The multi-level assessment and scoring specifically includes: Perform performance simulation tests on each candidate solution to predict its performance under the expected load; Perform a security scan to check for potential vulnerabilities and risks in the solution; Assess the rationality of the architecture, including service boundary delineation, interface contract design, and system maintainability; Estimate implementation costs, including development workload, operational complexity, and resource consumption; The evaluation results are weighted based on preset weights to generate a comprehensive score. The scoring was corrected and optimized by combining the implementation effect data of similar historical cases.

10. The API interface development and management method according to claim 7, characterized in that, The closed-loop optimization specifically includes: The actual performance data, user feedback, and operation and maintenance indicators after the API went live were compared and analyzed with the predicted data during the solution evaluation phase. Identify biases and deficiencies in the evaluation model, and adjust the weights of evaluation dimensions or introduce new evaluation indicators. Validated optimization patterns, technology selections, and architecture decisions will be stored in a historical case library. When encountering similar requirements again, prioritize design patterns and evaluation strategies that have been proven successful in the past.

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