A guide from Machine Learning Mastery argues that the riskiest part of an AI agent isn't the model itself but the orchestration layer — the glue that coordinates tool calls, memory, and external systems. Before deployment, the article recommends running seven regression tests designed to catch failures in that layer, where bugs tend to surface only after the agent is in the wild.
The tests are concrete rather than theoretical, aimed at scenarios like repeated tool invocations, unexpected input formats, and state drift across turns. The emphasis is on regression: verifying that a change to one component doesn't silently break another. This matters because orchestration bugs often produce plausible but wrong behavior, which is harder to spot than a hard crash.
Since this is the only source, there are no conflicting views to compare. The guide's core claim — that orchestration-layer regression testing deserves explicit attention before deployment — stands on its own as a practical checklist for agent builders. The article does not cover model-level evaluation, which it implicitly treats as a separate concern.