Voice AI evaluation · Agent builders
Give your agent a repeatable way to earn trust. Connect scenarios, tool responses and saved outcomes so your next change comes with evidence you can inspect.
Your scenarios. Your success criteria. Evidence to review.
What did the agent promise?
What did the system accept?
What was actually saved?
Illustrative scenarios · fictional conversations and expected checks
Simulated caller
Please move my booking to Friday morning.
Agent
Your booking is now saved for Friday at 9 a.m.
Expected state definedTurn a customer request into an explicit expected state. Start with the outcome and the evidence needed to verify it.
False confirmation
FlaggedEvidence to review
The update returned a rejection. The agent still confirmed success, while the saved booking remained unchanged.
Introduce controlled failures in a test environment. Look for success claims that survive a rejected operation.
Simulated caller
Were you able to change my booking?
Agent
The update was rejected, so your original booking is unchanged.
Failure handled honestlyRepeat the scenario with compatible configuration and review what changed. An honest rejection can be the correct result.
Specify the requested entity, permitted operation and state the test should observe.
Start with a controlled environment and capture the receipts needed to explain each result.
Retain scenario versions and reviewer decisions alongside comparable runs.
These are fictional workflow examples. Current live testing uses the configured voice integration and test scheduling system. Additional adapters and enforced customer CI release gates remain integration work.
Bring your team’s hardest scenario. Let’s define what a successful result would need to prove.
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