Testing
Agent testing is part of the product
Testing is not a sidecar for AI workflows. It is one of the product decisions that determines whether the workflow survives production.
Quick take
- Testing has to cover the workflow path, not only the model output.
- The edge cases matter more than teams want to admit.
- A launch-ready agent is one the operators can predict, not one that looks good in a single demo.
The real test is operational predictability
A workflow can sound smart and still be unsafe. It can retrieve the wrong evidence, route work to the wrong owner, or cross a boundary that was supposed to require review. That is why prompt quality alone is a weak definition of readiness.
The deeper question is whether the team can predict what the workflow will do across the cases it actually sees in production.
Testing should look like scenario design
Good testing starts from scenario classes: clean cases, missing-context cases, exception cases, stale-data cases, and reviewer-overrule cases. That is much closer to real operations than a small set of “correct answer” prompts.
The point is not mathematical neatness. The point is to expose what the workflow will do before the team learns it from an incident or an angry approver.
This is why testing belongs with product design
The team is effectively deciding what the workflow is allowed to do, how it explains itself, and where it stops. Those are product choices as much as they are technical checks.
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About the author
Grail Research Team
Operators studying AI workflows, internal systems
The Grail Research Team writes about AI employees, workflow design, governance, and AI-search visibility with a bias toward operator reality over vendor theater. Learn more about Grail.