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arXiv · 2606.01365

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

Xianyou Li·Weiran Yan·Yichao Wu·Penghao Liang·Mengwei Yuan·Jianan Liu·Jing Yang
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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.

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Xianyou Li, Weiran Yan, Yichao Wu, Penghao Liang, Mengwei Yuan, Jianan Liu, Jing Yang. 2026-06-15. Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability. https://arxiv.org/abs/2606.01365

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