Artificial Intelligence #multi-agent systems#llms
Early Diagnosis of Wasted Computation in Multi-Agent LLM Systems via Failure-Aware Observability
A research paper proposes a trace-based observability framework for multi-agent LLM systems that diagnoses wasted computation before final evaluation. On 165 GAIA traces, warned failed runs spent 58.1% of tokens after the first warning. A pilot using warnings reduced post-warning token fraction from 0.638 to 0.304, supporting a layered design with cheap online signals and deeper semantic checks.
Jun 16, 2026 1 source