AI Engineer · November 4, 2026

Behavior specifications: replay failed agent decisions with controlled context

video Model Evaluation AI Engineering Agent Security

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Why it matters

Mitchell Troyanovsky describes checking important process requirements against agent trajectories, including runs without a known final answer. Reviewers first determine whether a behavior applies, then whether the agent followed it. Forking a failed run can help compare context changes near the decision; a cheaper simulated agent is a diagnostic approximation, not proof of production reliability.

My takeaway: Write a small set of observable requirements, such as explaining a reconciliation mismatch instead of inventing an entry. Label applicability and compliance separately. Replay failures in an isolated copy with matching tools and data, change one context element, and compare repeated runs. Confirm simulation findings in the real harness; treat an agent’s explanation as a hypothesis.

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