# Knowledge-Guided Multi-Agent Framework for Automated Requirements Development: A Vision

arXiv: https://arxiv.org/abs/2506.22656 · HTML: https://mlsanity.com/paper/2506.22656/

## 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 11 of its 21 references that are in the map.
- First posted on arXiv 27 Jun 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, 11 of 11)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Source Code Summarization in the Era of Large Language Models](https://mlsanity.com/paper/2407.07959/) (2024)
- [MARE: Multi-Agents Collaboration Framework for Requirements Engineering](https://mlsanity.com/paper/2405.03256/) (2024)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [An Evaluation of Requirements Modeling for Cyber-Physical Systems via LLMs](https://mlsanity.com/paper/2408.02450/) (2024)
- [GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training](https://mlsanity.com/paper/2406.11439/) (2024)
- [Multi-Label Requirements Classification with Large Taxonomies](https://mlsanity.com/paper/2406.04797/) (2024)

## Abstract (arXiv)

This paper envisions a knowledge-guided multi-agent framework named KGMAF for automated requirements development. KGMAF aims to address gaps in current automation systems for SE, which prioritize code development and overlook the complexities of requirements tasks. KGMAF is composed of six specialized agents and an artifact pool to improve efficiency and accuracy. Specifically, KGMAF outlines the functionality, actions, and knowledge of each agent and provides the conceptual design of the artifact pool. Our case study highlights the potential of KGMAF in real-world scenarios. Finally, we outline several research opportunities for implementing and enhancing automated requirements development using multi-agent systems. We believe that KGMAF will play a pivotal role in shaping the future of automated requirements development in the era of LLMs.