Making AI Agent Runs Reproducible, Reviewable, and Testable
Every generation of software has its own version of “it worked on my machine.” For AI agents, the phrase has become, “It worked on my prompt.” A developer runs an agent locally, sees a convincing answer, and assumes the system is behaving correctly. The same agent may then behave differently in a pull request, with a slightly different input, after a model update, or inside a longer workflow. The final response can still look polished even when the process behind it is wrong.
A support agent might generate an answer before retrieving the relevant policy, call the same tool twice, use an unapproved fallback, and still return plausible text. An output-only test may pass, and a flat log may contain every event, yet neither necessarily shows that the agent took an invalid path. This is not only a model-quality problem; it is an evidence problem. Teams need to know what...
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