arXiv:2606.08275v1 Announce Type: new Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure. The obvious heuristics are wrong: the step that executes the harmful action is usually not the step that decided on it, and LLM-judge attribution is correlational and unreliable (state-of-the-art step-level accuracy on the Who&When benchmark is about 14%). We present Causal Agent Replay (CAR), which answers the question by
As LLM agents become increasingly autonomous and integrated into critical workflows, the need for robust debugging and explainability tools to understand and prevent failures is immediate.
This research directly addresses a core challenge in the deployment and scaling of AI agents: reliably identifying and rectifying the root causes of their failures, which is crucial for safety, trust, and wider adoption.
The introduction of Causal Agent Replay (CAR) shifts the paradigm from simple observability or evaluation of LLM agent failures to a causal attribution framework, enabling more precise debugging and improvement.
- · AI Agent Developers
- · Enterprises deploying LLM Agents
- · AI Safety Researchers
- · Software Debugging Tools
- · Companies with unreliable LLM agents
- · Traditional, non-causal debugging methods
Improved reliability and safety of LLM-based autonomous agents.
Accelerated adoption of AI agents in sensitive and high-stakes domains due to enhanced explainability and debuggability.
New regulatory frameworks and certification processes for AI agents may incorporate causal attribution capabilities as a requirement.
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Read at arXiv cs.LG