AI agents / Simulations
Test customer support AI.
Before you go live.
The new prompt answers the refund question, but skips the tool needed to issue the refund.
Turn real customer failures into simulations. Test prompts, knowledge, and tools before release, then compare the outcomes that matter.
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A better answer can still leave the task unfinished.
A change can fix one conversation and break another. Happy-path tests miss the tool failures, edge cases, and handoffs customers encounter.
Your tests miss the situations that fail in production.
A prompt or model change introduces a new regression.
A passing score does not show whether the task was completed.
Replay real failures.
Validate the change.
Start with where customers got stuck.
Use failed conversations and human recoveries to build relevant tests. Cover confusing requests, missing knowledge, failed tools, and difficult handoffs.
Replay production failures against the proposed agent version.
64% Passed36% Review
151Scenarios that changed from pass to fail
Test the same journey before and after.
Evaluate proposed prompt, knowledge, model, or tool changes against the same scenarios. See what improves, what still fails, and what regresses.
Did the refund fix pass the test?
ChatCompare the same customer journey:
Baseline: refund tool not called
Candidate: refund tool called
Check: customer outcome verified
Next: review edge-case results
Know what is ready for release.
Review task completion, accuracy, customer effort, and policy adherence. Give your team evidence to approve a change or send it back for more work.
Slack integrationRulebaseAPP8:01 AMThe refund scenario passes, but a tool timeout still leaves the task unresolved. Review before approving the release.
Test what matters to your customers.
What you test
Real journeys and proposed changes, side by side.
Failed journeys
Human recoveries
Prompt versions
Knowledge changes
Tool behavior
Edge casesWhat you compare
Task outcomes, not just how convincing the answer sounds.
Task completionAnswer accuracyCustomer effortTool successHandoff qualityPolicy adherenceWhat your team gets
A clear view of improvements and remaining risks before release.
Version comparisonsRegression evidenceFailed scenariosRelease reviewCommon questions
How do you test a customer support AI agent?
Start with representative customer journeys and known failures. Run the same scenarios against the baseline and proposed agent version, then compare task completion, answer accuracy, tool use, handoffs, and policy adherence. Review regressions before release and monitor the change in production.
Which scenarios should an AI agent test include?
Include routine requests, ambiguous questions, missing information, unavailable tools, repeated troubleshooting, and requests that need a human. A useful test checks the required action and customer outcome, not only whether the final answer sounds correct.
Connect testing to production outcomes
Find test scenarios with AI agent monitoring →
Use failed resolutions, repeat contacts, and human handoffs to identify the journeys your tests need to cover.
Turn test findings into agent improvements →
Review proposed fixes, compare the results, and measure the effect after release.
Ship with evidence, not guesswork.
Test your next agent change against the customer journeys it needs to resolve.
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