Adaptive Influence Graphs for Failure Attribution in Multi-Agent Systems
Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing efforts to automate failure attribution, diagnosing failed runs still largely relies on human engineers. Yet engineers rarely debug complex systems by reading raw logs end to end. Instead, observability tools organize traces around components, actions, and dependencies to support targeted navigation. We hypothesize that modern LLMs can benefit from the same paradigm. To test this hypothesis, we introduce Adaptive Influence Graphs (AIGs), a two-stage agentic framework that first transforms a failed trace into a structured graph and then navig...
| Authors | Yarden Bakish, Amir Dudai, Roy Ganz et al. |
|---|---|
| Primary category | cs.AI |
| Published | 25 August 2026 |
| Last updated at source | 25 August 2026 |
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