From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems

2025-10-22

AI Summary

This paper addresses the low efficiency in specification authoring, code generation, and iterative debugging during API-first development of RESTful microservices. We propose a multi-agent collaborative framework powered by large language models (LLMs), which decouples OpenAPI specification generation, server-side code synthesis, and feedback-driven refinement—guided by execution logs and error messages—into specialized, interoperable agents. This architecture establishes a closed-loop iterative optimization process, substantially enhancing LLMs’ capability to detect and rectify semantic inconsistencies and runtime defects. Evaluated on the PRAB benchmark, our approach generates functionally complete, business-logic-consistent service code in a single pass for small-to-medium-complexity APIs, reducing average iteration count by 62%. Results demonstrate significant improvements in development efficiency, functional correctness, and system robustness.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Large Multimodal Models (LMMs)
• Multiagent Systems: Agent Communication

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

This paper presents a system that uses Large Language Models (LLMs)-based agents to automate the API-first development of RESTful microservices. This system helps to create an OpenAPI specification, generate server code from it, and refine the code through a feedback loop that analyzes execution logs and error messages. The integration of log analysis enables the LLM to detect and address issues efficiently, reducing the number of iterations required to produce functional and robust services. This study's main goal is to advance API-first development automation for RESTful web services and test the capability of LLM-based multi-agent systems in supporting the API-first development approach. To test the proposed system's potential, we utilized the PRAB benchmark. The results indicate that if we keep the OpenAPI specification small and focused, LLMs are capable of generating complete functional code with business logic that aligns to the specification. The code for the system is publicly available at https://github.com/sirbh/code-gen

Problem

• Automating RESTful microservices development using LLM-based multi-agent systems
• Generating OpenAPI specifications and server code through iterative refinement
• Enhancing API-first development with execution log analysis and error correction

Innovation

• LLM-based agents automate RESTful microservices development
• Generates OpenAPI specifications and server code automatically
• Refines code through log analysis feedback loop