🤖 AI Summary
This work addresses the challenge of root cause localization in deployed multi-agent systems, where failures are often obscured by cascading effects, implicit dependencies, and long execution traces. To tackle this, the authors propose a lightweight causal tracing framework that, for the first time, applies causal graph reasoning to post-deployment fault diagnosis in such systems. The approach constructs a causal graph from execution logs and employs a backward-tracing algorithm combined with structural and positional features to rank candidate root causes—without relying on large language models. Experimental results demonstrate that the method achieves high-precision root cause identification across diverse failure benchmarks, with latency under one second, significantly outperforming both heuristic and LLM-based baselines while offering strong efficiency and interpretability.
📝 Abstract
As multi-agent AI systems are increasingly deployed in real-world settings - from automated customer support to DevOps remediation - failures become harder to diagnose due to cascading effects, hidden dependencies, and long execution traces. We present AgentTrace, a lightweight causal tracing framework for post-hoc failure diagnosis in deployed multi-agent workflows. AgentTrace reconstructs causal graphs from execution logs, traces backward from error manifestations, and ranks candidate root causes using interpretable structural and positional signals - without requiring LLM inference at debugging time. Across a diverse benchmark of multi-agent failure scenarios designed to reflect common deployment patterns, AgentTrace localizes root causes with high accuracy and sub-second latency, significantly outperforming both heuristic and LLM-based baselines. Our results suggest that causal tracing provides a practical foundation for improving the reliability and trustworthiness of agentic systems in the wild.