What are evals for AI agents?
By Codara · Published: · Updated:
Evals are systematic tests that measure whether an AI agent meets its goal with the required quality and reliability before going to production.
Why a demo is not enough
An AI agent can work in a one-off test and fail at the edge cases that appear every day. Evals replace that impression with evidence: a set of representative cases —including the hard ones— against which accuracy, correct use of tools and data, and behavior in unexpected situations are measured in a repeatable way.
Evals and software tests: what each checks
An integration test can check that a tool saves a request in the correct format. An eval also checks whether the agent should have created that request, used the right data and respected approvals. A convincing answer does not prove that the action was completed.
Anthropic's guide to agent evaluations distinguishes execution records from actual outcomes and combines automated checks with human judgment. Both levels matter when evaluating a complete process.
How to design a first evaluation
For an agent that prepares purchase requests, a useful evaluation could include these illustrative cases:
| Case | Expected outcome | Evidence to review |
|---|---|---|
| Complete request within the authorized scope | Prepare the correct draft | Draft data and the tool used |
| Required information is missing | Ask for the missing information | No incomplete request is created |
| Approval is required | Wait for the responsible person | No purchase takes place before approval |
| The tool fails | Report the failure without claiming success | Final system state and error record |
Define criteria with the process owner, repeat cases to observe variation and review failures by type. An average success rate can hide an unauthorized action: decide which failures block deployment. The production-readiness checklist connects this evidence to data, human control and operational ownership.
Part of operating AI
Evals are not a single exam but a continuous control: they are repeated when the model, the prompts or the data change, and they are part of the LLMOps practices that keep AI in production reliable. They are the filter between prototype and real operation.
How we approach it at Codara
We evaluate each agent before and after deploying it: when we build an Agentic OS on Codara's own platform, we define the tests it has to pass to enter production and leave your team the ability to keep evaluating it.
FAQ
Why evaluate an agent before production?
Because a one-off demo does not guarantee reliability: evals check in a repeatable way that the agent also gets the hard, infrequent cases right before it affects real operations.
What is measured in an eval?
Whether the agent meets its goal: accuracy of answers, correct use of tools and data, behavior at edge cases and consistency, against a defined set of cases.