Development and verification of an orchestration architecture of AI agents for automated API testing with a unified representation of requirements

The object of this research is the process of automated testing of application programming interfaces (API) in systems developed following Agile and DevOps approaches, where requirements are heterogeneous, frequently changing, and represented in various formats: from formal OpenAPI specifications to textual documentation (Confluence). The problem addressed is the absence of an architectural mechanism for systematic integration of heterogeneous requirements sources and for decoupling requirements interpretation from test generation. Specification-oriented approaches fail to incorporate business rules from textual sources, covering only 70–80% of specification content. LLM-based approaches are unstable: repeated runs with identical prompts yield test sets differing by 20–40%, with coverage standard deviation reaching ±12%. AI-driven orchestration architecture is proposed, comprising a coordination layer O, requirements-processing agents A, and a protocol-independent unified requirements representation R. The test generation process is formalized as T = G(O(A(S))), where S denotes the set of requirements sources and G the deterministic test generation algorithm. The key property of the architecture is isolation of the stochastic LLM component at the agent level, guaranteeing reproducibility of the test set T for any fixed R. Verification was conducted through a comparative experiment on a REST API service with 5 endpoints and 12 business rules. API coverage: 88–92% vs. 72–78% (specification-based) and 55–82% (LLM-based). Standard deviation: ±2% vs. ±3% and ±12%. Reproducibility: 0.97 vs. 0.95 and 0.62. Maintenance: 15–20% modified tests vs. 60–70% and 40–55%. The proposed architecture targets software development teams practising API-First Development and Documentation-Driven Development. Results are applicable to Agile/DevOps environments with frequently changing requirements.