Preventing Error Propagation in Multi-Agent AI through Runtime Monitoring
Shahnewaz Karim Sakib, Anindya Bijoy Das
Why It Matters
What makes this one worth your time
Understanding how multi-agent systems can both improve and jeopardize decision-making is crucial for developing robust AI applications in various domains.
A framework to enhance reliability in multi-agent AI through reasoning exchange.
Summary
The paper investigates the reliability of multi-agent AI systems by developing a framework for agents to independently answer questions, share reasoning traces, and revise their decisions, while evaluating the impact on accuracy and error propagation.
Key contributions
- Development of a framework for multi-agent reasoning exchange and decision revision.
- Empirical evaluation of the framework's effectiveness in improving accuracy and managing error propagation.
Notable insights
- The dual nature of reasoning exchange in multi-agent systems can both correct and propagate errors, highlighting the need for careful monitoring.
- The framework's evaluation across diverse domains suggests potential for broad applicability, though specific domain challenges may vary.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2606.29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and support a final decision. However, such communication may also introduce reliability risks: reasoning from one agent can correct another agent's mistake, but it can also mislead an agent that was initially correct. This paper studies reliable multi-agent AI communication through reasoning exchange and runtime answer revision. We develop a framework in which agents first answer multiple-choice questions independently, then share reasoning traces and revise their decisions. We conduct numerical experiments where we evaluate whether this process improves accuracy, produces more positive than negative answer transitions, and remains effective across domains such as cybersecurity, networking, and general knowledge. The results help identify when multi-agent reasoning improves reliability and when it may propagate errors.