Evaluate an AI Agent Before Trusting It
Build a repeatable test before you deploy the agent, so a model update, a prompt tweak, or a tool integration change doesn't silently break its judgment without anyone noticing.
Download workflow config (JSON)How the data moves
This is a handoff plan, not an automatic integration. Review outputs before the next step. Unlabeled arrows show order, not a verified data format. Use the capability links to explore tool substitutions.
Step 1
Build a Fixture Task Set From Real Cases
Collect 15-25 representative tasks the agent will actually face, each with a known-correct answer or an acceptable range, drawn from real historical cases rather than invented scenarios. This fixture set is the ground truth everything else in this workflow measures against -- a fixture set built from convenient, easy examples will pass every time and tell you nothing.
Tools
No catalog tool specified
Step 2
Add Adversarial and Edge Cases With Giskard
Use Giskard's automated test generation to add adversarial inputs to the fixture set: prompt injections, off-topic requests, ambiguous phrasing, and inputs designed to surface hallucinations. A friendly test set only ever tells you the agent works when everything goes right; this is what catches it before a real user does.
Step 3
Score the Agent Against the Full Set in Braintrust
Run the fixture set through the agent using Braintrust, scoring each output against defined criteria, correctness, whether it called the right tool, whether the output format is usable downstream, instead of eyeballing a handful of outputs and calling it good.
Step 4
Set a Regression Baseline
Save the current pass rate as the baseline and re-run the full fixture set every time the underlying model updates, the prompt changes, or a connected tool's API changes. Wire this to run automatically rather than relying on remembering to do it by hand -- a manual step is a step that eventually gets skipped right before the change that actually breaks something.
Step 5
Log Every Failure With Enough Context to Debug It
Every failing case gets logged with the input, the agent's actual output, the expected output, and its tool calls if visible, enough for a person to diagnose what went wrong, not just a pass or fail flag with no trail behind it.
Tools
No catalog tool specified
Time Required
3-5 hours, one-time setup
Expected Result
A fixture-task test suite with a scored baseline that re-runs automatically whenever the agent's model, prompt, or tools change, flagging any drop in quality before it reaches real users.
Maintenance Reality
Every multi-tool workflow needs upkeep. Here's what this one typically needs.
- Typical upkeep
- 1-2 hrs/month
- Single point of failure
- The fixture dataset itself -- if it's lost, never updated, or quietly narrowed down to only the easy cases, the whole evaluation stops catching anything real.
What typically breaks
- • The fixture set goes stale as the agent's real task mix shifts, so a passing score stops meaning what it used to.
- • A model-provider update changes behavior silently if the regression re-run isn't wired to trigger automatically, so the drop goes unnoticed until a user hits it.
Handover tip: Keep the fixture set and its expected answers in version control next to the agent's config, not in a separate spreadsheet nobody remembers to update, so a successor can see exactly what 'passing' has meant over time.
Related Workflows
Automate Local Dev Tasks Without Paying for an API
Set up a local coding agent that handles repetitive technical work, file cleanup, log parsing, batch renaming, using free open-weight models instead of a metered frontier API, then chain the output into a free automation platform so results land where your team actually looks.
View workflowBuild a Custom AI Agent for Your Business Like an Enterprise Copilot Studio Team
Build and deploy your own custom AI agent in about half a day.
View workflowDeploy a Customer-Facing Support Agent Like an Enterprise Contact Center
Get a trained support agent live on your site in about two hours.
View workflow