How can rogue agents in LLMs be prevented?
Preventing Rogue Agents Improves Multi-Agent Collaboration
February 11, 2025
https://arxiv.org/pdf/2502.05986This paper explores how to improve the collaboration and robustness of multi-agent AI systems, particularly in scenarios where a single malfunctioning "rogue" agent can cause the entire system to fail. It introduces a method to monitor agents for signs of likely failure (e.g., high uncertainty in action selection) and intervene by resetting the communication channel or environment state to prevent cascading errors.
Key points for LLM-based multi-agent systems:
- Rogue agents, even a single one, can severely degrade performance.
- Monitoring agent uncertainty (entropy, varentropy, kurtosis of action probabilities) can effectively predict failures.
- Simple interventions, like communication resets, are sufficient to significantly improve multi-agent collaboration.
- This approach improves performance across different LLMs, communication structures (symmetric and asymmetric), and task complexities. It generalizes across varying numbers of agents and doesn't require retraining the monitoring mechanism.
- Hallucinations are a frequent cause of rogue agent behavior in LLM-based systems.