# Aegis:An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering

arXiv: https://arxiv.org/abs/2410.12475 · HTML: https://mlsanity.com/paper/2410.12475/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 11 of 11)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Editing Factual Knowledge in Language Models](https://mlsanity.com/paper/2104.08164/) (2021)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://mlsanity.com/paper/2406.04692/) (2024)
- [Editing Large Language Models: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2305.13172/) (2023)
- [A Review on Language Models as Knowledge Bases](https://mlsanity.com/paper/2204.06031/) (2022)
- [Task and Motion Planning with Large Language Models for Object Rearrangement](https://mlsanity.com/paper/2303.06247/) (2023)
- [Can ChatGPT and Bard Generate Aligned Assessment Items? A Reliability Analysis against Human Performance](https://mlsanity.com/paper/2304.05372/) (2023)

## Built on by (most cited first, 7 of 7)

- [An Empirical Study of Agent Developer Practices in AI Agent Frameworks](https://mlsanity.com/paper/2512.01939/) (2025)
- [Survey of GenAI for Automotive Software Development: From Requirements to Executable Code](https://mlsanity.com/paper/2507.15025/) (2025)
- [LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems](https://mlsanity.com/paper/2501.11864/) (2025)
- [The Competence Shadow: Theory and Bounds of AI Assistance in Safety Engineering](https://mlsanity.com/paper/2603.25197/) (2026)
- [Multi-Agentic Approach for History Matching of Oil Reservoirs](https://mlsanity.com/paper/2605.15028/) (2026)
- [From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devices](https://mlsanity.com/paper/2608.12025/) (2026)
- [SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessment](https://mlsanity.com/paper/2609.20584/) (2026)

## Abstract (arXiv)

Functional safety is a critical aspect of automotive engineering, encompassing all phases of a vehicle's lifecycle, including design, development, production, operation, and decommissioning. This domain involves highly knowledge-intensive tasks. This paper introduces Aegis: An Advanced LLM-Based Multi-Agent for Intelligent Functional Safety Engineering. Aegis is specifically designed to support complex functional safety tasks within the automotive sector. It is tailored to perform Hazard Analysis and Risk Assessment(HARA), document Functional Safety Requirements(FSR), and plan test cases for Automatic Emergency Braking(AEB) systems. The most advanced version, Aegis-Max, leverages Retrieval-Augmented Generation(RAG) and reflective mechanisms to enhance its capability in managing complex, knowledge-intensive tasks. Additionally, targeted prompt refinement by professional functional safety practitioners can significantly optimize Aegis's performance in the functional safety domain. This paper demonstrates the potential of Aegis to improve the efficiency and effectiveness of functional safety processes in automotive engineering.