Verification gives an agent a feedback signal that doesn’t come from the model itself, so it can check its work against reality. Evals do the same for the people building the agent.
For the agent
Models are confident, which is not the same as correct. Effective agents check their output:
- Coding agents run tests, type checkers and linters, and read the errors.
- Browser agents take screenshots of what actually rendered.
- Research agents cite sources and re-read them.
An agent that can run the tests fixes its own bugs. One that can’t ships them confidently.
For the builder
Evals are a set of realistic tasks with checkable outcomes, run every time a prompt, tool or model changes. Without them, every change is a guess.
As architecture
The review-and-critique (generator–critic) pattern builds verification into the system: a separate critic agent checks output against criteria, or runs tests, before it’s accepted. Each revision cycle costs another model call. See agent design patterns.
Note: the Google Cloud pattern guide gives no evaluation guidance, so builder-side evals still rest on the agents post.