AgentLocate framework pinpoints which AI agent caused a system failure
When multi-agent AI systems break, who’s at fault? AgentLocate finds the culprit.
Deep Dive
The paper presents AgentLocate, a framework for failure localization in LLM-based multi-agent systems. It combines an LLM-based judge with multi-perspective verification from independent evaluators, aggregated via a confidence-aware strategy, to identify both the responsible agent and the earliest decisive step. Evaluated on two complementary benchmarks, AgentLocate outperforms existing methods in identifying responsible agents and failure steps while maintaining low token usage and running time.
Key Points
- AgentLocate identifies both the responsible agent and the earliest failure step in multi-agent LLM systems.
- Uses a judge LLM with multi-perspective verification from independent evaluators and confidence-aware aggregation.
- Outperforms existing methods on two benchmarks while remaining efficient in token usage and runtime.
Why It Matters
As multi-agent AI systems enter production, AgentLocate provides the debugging tools needed to maintain reliability at scale.