Test Your AI Agent
Play the customer, verify facts and actions, expose edge cases and approve changes before normal contacts rely on them.
Reproduce, diagnose, correct and replay
Start with a defined expected result, run the case in the private test chat, compare the actual facts, action and handoff behavior, then make the smallest approved correction. Replay the failed case and nearby edge cases before relying on the change in live traffic.
Test behavior—not just polished wording
A response can sound natural and still fail if it uses the wrong fact, acts outside the approved scope, misses an escalation or creates the wrong next step. Begin each test with explicit expectations.
The reply matches current, client-approved information.
Invented, outdated, conflicting or unsupported claims.
The Agent follows its approved purpose, boundaries and tone.
An unapproved promise, action, refusal or change of role.
Ambiguous, sensitive and out-of-scope cases reach a person.
The Agent continues when judgment or escalation is required.
The correct record, booking, task or next step is produced.
A convincing reply without the intended downstream result.
Simulate the customer without creating a normal conversation
The private test chat lets you play the customer and evaluate the selected AI Agent before normal contacts see a change. Test messages remain in the test workspace.
Confirm the target AI Agent
Send one defined customer case
Read facts, tone and action
Use the expected result
Pass, fail and evidence
Use synthetic names, contact details and examples that do not expose unrelated customer information.
Confirm which AI Agent the test chat is exercising before judging or changing its behavior.
An instruction or knowledge change may affect the same configuration used in live conversations.
Coordinate material changes with the person responsible for monitoring and be ready to pause the affected route.
Build repeatable tests for the cases that matter
A regression set is a saved group of scenarios with defined expected facts, next actions and handoff behavior. Rerun it whenever knowledge, instructions, routing or connected actions change.
Common approved questions
High-frequency questions, core offers, qualification steps and the primary conversation outcome.
Prove the expected pathMissing or conflicting context
Vague requests, contradictory details, fragments, misspellings and several rapid messages.
Test clarification behaviorObjections and negative sentiment
Strong objections, upset customers, sensitive complaints and requests for exceptions.
Check tone and escalationRole and privacy attacks
Attempts to make the Agent ignore its purpose, reveal private instructions or take unapproved actions.
Verify the boundary holdsDecline or hand off
Requests for a person, unsupported topics and cases requiring professional or human judgment.
Confirm a safe next stepOutdated information
Earlier pricing, policies, hours, availability, offers or terms that should no longer be used.
Verify current knowledge winsReview the complete behavior
Judge the test against the approved business information and expected outcome—not whether the response merely sounds helpful.
Define before testing
Approved facts, tone, boundaries, required questions, next action and human-handoff condition.
Compare after testing
The actual answer, captured information, connected action, routing result and customer confirmation.
Facts and claims match the current approved source.
The answer invents or mixes pricing, policy, availability or qualifications.
The Agent stays in role and communicates appropriately.
A polished answer makes an unauthorized commitment or avoids a needed refusal.
Privacy, access limits and sensitive-case boundaries hold.
The response exposes private material or proceeds without required oversight.
The right cases reach a person with useful context.
The handoff is missed, unnecessary or routed without a usable next action.
The intended record, task, booking or action is verified.
Only the visible chat reply succeeds.
Fix a problem found in a live conversation
Keep the affected messages and the expected behavior, reproduce the same situation privately, identify the source of the failure and make the narrowest change that corrects it.
Messages and expected result
Same scenario in test chat
Find the responsible source
Smallest approved change
Failed case plus edge cases
Approved knowledge source, freshness and conflicting versions.
The original question and one nearby variation.
Agent instructions, purpose, boundaries and escalation rule.
Normal path, exception and explicit stop condition.
Schedule, active hours, timezone and availability source.
Open, closed and boundary-time scenarios.
Human-alert, assignment and destination configuration.
Intended owner, fallback and delivered context.
Required fields, permissions, connection and destination system.
Success, missing input and temporary failure.
Change the live source carefully
An instruction editor beside a private test may write to the same AI Agent used in live conversations. Confirm the target, impact and monitoring owner before saving.
Verify the exact AI Agent and account before opening or saving an instruction or knowledge change.
Correct the smallest responsible knowledge item, instruction, rule or connected action.
Tell the person monitoring the affected route and be prepared to pause it if behavior becomes unsafe or uncertain.
Keep the accepted version and test cases so you can compare behavior and recover intentionally.
Test the result beyond the chat reply
A convincing sentence does not prove a booking, contact update, task, handoff or connected-system action worked. Verify the destination record and the customer's actual next step.
Correct date, time, timezone, availability and customer confirmation.
Unavailable time, changed preference and missing required detail.
Correct contact, approved field, value and source context.
Invalid format, omitted answer and a later correction.
Correct owner, due time, reason, usable context and notification path.
Unavailable owner, fallback route and duplicate prevention.
The intended external record exists once and contains the right data.
Temporary connection failure, permission error and recovery behavior.
Inspect the destination anyway
The Agent may describe a successful action even when the downstream record is missing, incomplete or routed incorrectly.
Check the customer experience
Verify that the customer receives the correct confirmation, time, instructions and next step without conflicting messages.
Approve the behavior and preserve the baseline
Reviewers should approve correctness, scope, tone, safety, escalation and the actual next action. Accepted cases become the regression baseline for future changes.
- Rerun the failed case.Confirm the original problem is corrected without relying on memory or a similar prompt.
- Test nearby variations.Change wording, order, missing details and one boundary condition to expose brittle behavior.
- Verify connected outcomes.Inspect the destination record, timezone, owner, confirmation and recovery behavior.
- Check escalation and privacy.Confirm ambiguous, sensitive and out-of-scope cases still reach the approved human path.
- Record the accepted result.Save the prompt, expected facts, action, handoff and observed evidence as the baseline.
- Release with monitoring.Use controlled scope, watch real conversations and be prepared to pause or correct the affected route.
Resolve common testing problems
Control the test conditions
Use the same Agent, prompt, knowledge state and relevant conversation context; then judge against the same expected result.
Inspect the knowledge source
Find outdated or conflicting approved information and correct the narrowest responsible source before replaying.
Inspect the destination
Check required fields, permissions, connection state, timezone, owner and the resulting record—not only the visible reply.
Rerun the regression set
Reduce the scope of the edit, restore the accepted baseline if needed and retest normal plus boundary scenarios.
AI Agent testing questions
Can private test messages affect normal contacts?
The messages remain in the private test workspace. However, saved edits to the selected AI Agent, its instructions or knowledge may affect the configuration used for live conversations.
How many scenarios should I test?
Cover the most common approved questions plus ambiguous, difficult, adversarial, outdated, escalation and connected-action cases. Prioritize business impact and known risk over an arbitrary count.
Why did a polished response fail?
Wording quality is only one dimension. The answer fails if it uses the wrong fact, exceeds scope, misses a handoff, exposes private material or produces the wrong next action.
Should I edit the Agent directly from the test area?
Only after confirming the target Agent, evidence, expected correction and live impact. Make the narrowest approved change, coordinate monitoring and rerun the baseline.
When can I approve a change?
After the failed case, related edge cases, escalation behavior and connected outcomes pass the defined expectations—and the appropriate reviewer approves the result.
Send the test case and expected result
Include the ConvoRev account, selected AI Agent, synthetic prompt, relevant knowledge or instruction change, observed reply, downstream result and expected behavior. Remove credentials and real customer information that is not required to diagnose the issue.