# Enforcement Agents: Enhancing Accountability and Resilience in Multi-Agent AI Frameworks

arXiv: https://arxiv.org/abs/2504.04070 · HTML: https://mlsanity.com/paper/2504.04070/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 10 references that are in the map.
- First posted on arXiv 5 Apr 2025.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [Large Language Model Agent: A Survey on Methodology, Applications and Challenges](https://mlsanity.com/paper/2503.21460/) (2025)
- [Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2311.13165/) (2023)
- [Agent S: An Open Agentic Framework that Uses Computers Like a Human](https://mlsanity.com/paper/2410.08164/) (2024)
- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [Chemist-X: Large Language Model-empowered Agent for Reaction Condition Recommendation in Chemical Synthesis](https://mlsanity.com/paper/2311.10776/) (2023)
- [Generative AI Agents in Autonomous Machines: A Safety Perspective](https://mlsanity.com/paper/2410.15489/) (2024)
- [Performance Evaluation of Tokenizers in Large Language Models for the Assamese Language](https://mlsanity.com/paper/2410.03718/) (2024)
- [Goal Conflict in Designing an Autonomous Artificial System](https://mlsanity.com/paper/1703.06354/) (2017)

## Built on by (most cited first, 4 of 4)

- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [Internet of Agents: Fundamentals, Applications, and Challenges](https://mlsanity.com/paper/2505.07176/) (2025)
- [Governance-as-a-Service: A Multi-Agent Framework for AI System Compliance and Policy Enforcement](https://mlsanity.com/paper/2508.18765/) (2025)
- [Multi-Stakeholder Alignment in LLM-Powered Collaborative AI Systems: A Multi-Agent Framework for Intelligent Tutoring](https://mlsanity.com/paper/2510.23245/) (2025)

## Abstract (arXiv)

As autonomous agents become more powerful and widely used, it is becoming increasingly important to ensure they behave safely and stay aligned with system goals, especially in multi-agent settings. Current systems often rely on agents self-monitoring or correcting issues after the fact, but they lack mechanisms for real-time oversight. This paper introduces the Enforcement Agent (EA) Framework, which embeds dedicated supervisory agents into the environment to monitor others, detect misbehavior, and intervene through real-time correction. We implement this framework in a custom drone simulation and evaluate it across 90 episodes using 0, 1, and 2 EA configurations. Results show that adding EAs significantly improves system safety: success rates rise from 0.0% with no EA to 7.4% with one EA and 26.7% with two EAs. The system also demonstrates increased operational longevity and higher rates of malicious drone reformation. These findings highlight the potential of lightweight, real-time supervision for enhancing alignment and resilience in multi-agent systems.