AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems

• URL: http://arxiv.org/abs/2603.14688v1
• Date: Mon, 16 Mar 2026 00:46:44 GMT
• Title: AgentTrace: Causal Graph Tracing for Root Cause Analysis in Deployed Multi-Agent Systems
• Abstract summary: We present AgentTrace, a lightweight causal tracing framework for post-hoc failure diagnosis in deployed multi-agent AI systems.<n>AgentTrace reconstructs causal graphs from execution logs, traces backward from error manifestations, and ranks candidate root causes using interpretable structural and positional signals.<n>Our results suggest that causal tracing provides a practical foundation for improving the reliability and trustworthiness of agentic systems in the wild.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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