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

arXiv: https://arxiv.org/abs/2510.19274 · HTML: https://mlsanity.com/paper/2510.19274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 43 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 18 of 18)

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- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [A Survey on Large Language Models for Code Generation](https://mlsanity.com/paper/2406.00515/) (2024)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [An LLM Compiler for Parallel Function Calling](https://mlsanity.com/paper/2312.04511/) (2023)
- [AI-powered Code Review with LLMs: Early Results](https://mlsanity.com/paper/2404.18496/) (2024)
- [Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis](https://mlsanity.com/paper/2402.01386/) (2024)
- [Asynchronous LLM Function Calling](https://mlsanity.com/paper/2412.07017/) (2024)
- [Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation](https://mlsanity.com/paper/2310.16263/) (2023)
- [Practices and Challenges of Using GitHub Copilot: An Empirical Study](https://mlsanity.com/paper/2303.08733/) (2023)
- [Microservice API Evolution in Practice: A Study on Strategies and Challenges](https://mlsanity.com/paper/2311.08175/) (2023)
- [LLM-Generated Microservice Implementations from RESTful API Definitions](https://mlsanity.com/paper/2502.09766/) (2025)
- [SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models](https://mlsanity.com/paper/2402.11625/) (2024)
- [TimeLess: A Vision for the Next Generation of Software Development](https://mlsanity.com/paper/2411.08507/) (2024)
- [Custom Developer GPT for Ethical AI Solutions](https://mlsanity.com/paper/2401.11013/) (2024)

## Abstract (arXiv)

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