Happy-path bias
Scripted flows miss live language, partial tools, and messy state.
AI agent testing
AI agent testing that starts from production failures.
Use real failed conversations to test tool use, error handling, and handoffs. Your reviewers define the correct result, and we compare the existing and changed agent before release.
Turn failed tests into cases your team can fix.
Record what the agent did, what it should have done, and how you'll test the correction. Include real requests as well as simulations.
Answer-only asserts
Checking the final string hides unsafe trajectories.
Missing expected result
A failed test is easier to fix when reviewers have recorded the correct reply or actions.
Unused QA findings
Turn recurring failures from tickets into repeatable tests.
Compare the fix with the current agent
We combine scenario design, production-trace replay, human-approved corrections, and before/after measurement on one agent workflow.
Tests from real mistakes
Turn recurring failures into repeatable tests and keep their version history.
Action checks
Score tool use, information handling, recovery, and handoffs alongside the final reply.
Test reviewed corrections
Record the approved response and test whether retrieving it helps on similar tasks.
Build tests you can repeat
Keep the task, prompt, tools, and expected result together so each release can be compared fairly.
Scenario versioning
Lock prompts, tools, and expected trajectories so releases compare fairly.
Mixed modes
Offline replay, controlled simulation, and targeted live review for high-risk clusters.
Review rules
Decide when a corrected test case can become an approved example for future tests or retrieval.
Test agent actions and tool useConnect tests to LLM evaluation services and release gates
Agent testing package
Turn production failures into versioned tests and approved fixes.
Scenario inventory
Map current tests against recurring production failures.
Action checks
Tool use, information handling, error recovery, handoffs, and safety.
Correction review
Rules for approving a corrected test case as an example for future use.
Regression gate outline
Version prompts, tools, and expected trajectories for release comparison.
Scorecard template
Start from the downloadable agent evaluation rubric CSV.
Free template
Download the AI agent evaluation rubric
Use the CSV as a testing scorecard for outcome, tools, state, recovery, escalation, safety, cost, and latency.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
Who this is for
- Teams shipping tool-using agents who need regression gates
- Support, voice, and ops agents with repeating multi-step failures
- QA leads who need reviewed corrections alongside failed tests
- Leaders comparing prompt patches vs retrieving approved experiences
Agent testing sprint
Review
Compare current tests with real failures and define checks for the agent's actions.
Build
Version test scenarios and approve corrections for one task.
Evaluate
Compare the existing and changed agent and recommend whether to release it.
Retest the correction
Ask a reviewer to approve the expected result, then test the fix on the original failure and separate related cases.
Limitations
- Simulation alone under-covers live language and tool noise.
- Pass/fail without an approved rewrite does not improve the next run.
- Start with one costly task so reviewers can define and check the expected result consistently.
- Retrieval experiments need held-out measurement before production expansion.
How we test changes
We review real conversations with your team and agree what the agent should do differently. Approved corrections can become training data, test cases, or examples the agent looks up. We compare each change with your best existing setup on separate test cases, then check for new failures in the software you plan to deploy.
References:
Common questions
What does the work include?
We combine scenario design, production-trace replay, human-approved corrections, and before/after measurement on one agent workflow.
Do you fine-tune our model?
If it's worth testing after we review the problem. We try your best prompt and consider better examples or tool fixes first. We test an adapter on cases it wasn't trained on and check for new failures. The contract sets delivery and usage rights; the base model's license still applies.
What do we need to start?
Logs or conversation exports for one task, examples of good results, and a reviewer who can approve corrections. We agree data handling before you send anything. Remove unnecessary personal details.
How is success measured?
We test the existing and changed agent on the same task. Where the data is available, we compare completed tasks, human edits, handoffs, response time, and cost.
Related
Book an AI agent testing review.
Bring your current tests and examples of recurring mistakes. We'll discuss the gaps and the work needed to test a fix.