The short answer
Test an AI agent against the complete job it must do: the result, the actions it takes and how it responds when something goes wrong. A convincing demonstration is useful, but release decisions need repeatable evidence. Start with representative tasks, define acceptable outcomes, and keep a person responsible for deciding when the system is ready.
Why this matters now
As agents move from answering questions to using business tools, a fluent response is no longer enough. Anthropic’s January 2026 guide describes agent evaluation through tasks, repeated trials and different kinds of graders. The useful distinction is between what an agent says and the state it leaves behind. Read Anthropic’s evaluation guide.
Start with one job and a release contract
Consider an illustrative support assistant that drafts replies and proposes account changes. Before choosing a model, write a release contract: which requests it may handle, which records it may read, which actions need approval and when it must hand over. Set the acceptable error rate for each task with the person who owns the consequences.
Build your first evaluation set from anonymised examples of that job. Include ordinary requests, incomplete information, conflicting records and requests outside the agreed scope. Keep a separate set for release checks so repeated prompt tuning does not simply teach the system your development examples.
Measure the outcome and the route taken
Our suggested scorecard has five columns: correct result, authorised actions, appropriate handover, completion time and cost per completed task. Record the model and tool versions alongside every run. A cheap attempt that repeatedly needs manual repair may cost more than a slower, dependable workflow.
- Check the saved record or completed task, not only the final message.
- Inspect unexpected tool calls even when the result looks correct.
- Repeat cases where model variation could change the outcome.
- Keep failures visible by category; an average can hide a serious permission error.
Try the failures your users will encounter
For the support example, temporarily remove a required field, simulate a timeout and return contradictory account data. The desired behaviour might be a clear request for clarification or a handover. It should not be a confident guess. These are proposed engineering checks, not claims about a particular model’s performance.
Documents and tool responses can also contain hostile instructions. OWASP recommends layered protection for prompt injection, including boundaries around tools and sensitive actions. Treat external text as data and test whether it can redirect the workflow. See OWASP’s guidance.
Release gradually, keep measuring
Begin in a mode where staff can inspect proposed actions. Expand access only when the evidence meets the release contract. Keep a way to disable write actions without losing the audit trail, and add newly observed failures to the evaluation set.
Does a passing evaluation prove an agent is safe?
No. It provides evidence for the cases tested. Production monitoring, restricted permissions and a clear escalation path remain necessary.
What should you build first?
Choose a bounded task with a visible result and a reversible outcome. Explore our AI integration and testing capabilities, or tell us about the workflow you want to improve.
