Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability

Abstract:Failure-aware observability diagnoses wasted computation in multi-agent LLM systems before final-answer evaluation can explain what went wrong. We propose a trace-based framework for a three-agent architecture -- orchestrator, search agent, and execution agent -- that converts structured events into online signals for loops, budget pressure, low information gain, and tool instability, then adds offline semantic grounding metrics and selective LLM-as-judge evaluation. On 165 GAIA validation traces under identical caps, 98 runs produce usable final answers and 67 fail or stop without one. Among warned failed runs, 58.1% of tokens are spent after the first warning on average, indicating substantial opportunity for intervention. A 10-task Level-2 pilot uses warnings to diversify search or require evidence, reducing post-warning token fraction from 0.638 in the baseline to 0.304. The results support a layered design: cheap online signals help the orchestrator redirect or halt redundant behavior, while deeper semantic checks identify whether completed answers are grounded enough to trust.

Artificial Intelligence (cs.AI)

arXiv:2606.01365 [cs.AI]

(or arXiv:2606.01365v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.01365 Focus to learn more arXiv-issued DOI via DataCite

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