POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems
Addresses the need for scalable, decentralized failure detection in safety-critical multi-agent LLM systems, a gap highlighted by emerging AI regulation.
POIROT repurposes a system's own agents as its diagnostic layer for failure detection in multi-agent LLM systems, outperforming single-LLM evaluator baselines with gains scaling with problem complexity (OR=1.60, p=0.008).
Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and hallucinations that resist characterisation block their deployment in safety-critical domains -- a gap made legally untenable by emerging AI regulation. Existing evaluation paradigms share a common flaw: centralised judgment creates single points of failure and demands domain-specific expertise. Here we present POIROT, a protocol that repurposes a system's own agents as its diagnostic layer, leveraging the epistemic diversity already present in the architecture. Across evaluated settings, POIROT outperforms single-LLM evaluator baselines, with gains that scale with problem complexity (OR = 1.60, $p = 0.008$), agent count, and fault dimensionality, persisting under compound fault conditions. These results demonstrate that safety oversight need not be externalised: the agents executing a role carry sufficient collective intelligence to audit it. We release POIROT as an open-source library alongside BLAME, a benchmark for fault attribution in safety-critical multi-agent systems.