mlsanityData to 7 Oct 2026

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

arXiv 2410.12475 ↗First posted 16 Oct 2024cs.MAcited by 7

On the map: LLM agents and tool use → Agent threats, guardrails and governance

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.

Key facts. Cited by 7 papers in the map (data through 7 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.

Builds on

11 of 23 references.

References · 11

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2021Editing Factual Knowledge in Language Modelscited by 548
  4. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 496
  5. 2023End-to-end Autonomous Driving: Challenges and Frontierscited by 485
  6. 2022Memory-Based Model Editing at Scalecited by 443
  7. 2024Mixture-of-Agents Enhances Large Language Model Capabilitiescited by 420
  8. 2023Editing Large Language Models: Problems, Methods, and Opportunitiescited by 367
  9. 2022A Review on Language Models as Knowledge Basescited by 156
  10. 2023Task and Motion Planning with Large Language Models for Object Rearrangementcited by 140
  11. 2023Can ChatGPT and Bard Generate Aligned Assessment Items? A Reliability Analysis against Human Performancecited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025An Empirical Study of Agent Developer Practices in AI Agent Frameworkscited by 14
  2. 2025Survey of GenAI for Automotive Software Development: From Requirements to Executable Codecited by 5
  3. 2025LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systemscited by 4
  4. 2026The Competence Shadow: Theory and Bounds of AI Assistance in Safety Engineeringcited by 0
  5. 2026Multi-Agentic Approach for History Matching of Oil Reservoirscited by 0
  6. 2026From Safety Documentation to Safety Knowledge Support: An Evidence-Grounded LLM Framework for Medical Devicescited by 0
  7. 2026SAFARI: An Industrial Benchmark for LLM-Assisted Hazard Analysis and Risk Assessmentcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Towards Automated Safety Requirements Derivation Using Agent-based RAG
  2. 2025Aegis2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails
  3. 2025MARBLE: A Multi-Agent Rule-Based LLM Reasoning Engine for Accident Severity Prediction
  4. 2026CRASH: Cognitive Reasoning Agent for Safety Hazards in Autonomous Driving
  5. 2024ChatScene: Knowledge-Enabled Safety-Critical Scenario Generation for Autonomous Vehicles
  6. 2024GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making
  7. 2026DriveSafe: A Hierarchical Risk Taxonomy for Safety-Critical LLM-Based Driving Assistants
  8. 2024Engineering Safety Requirements for Autonomous Driving with Large Language Models