Context
Caller asks whether a refill request was submitted after the agent started a tool call.
How-to · Voice AI testing
How to test voice AI agents in 5 steps.
Choose calls for one task, write clear scoring rules, review the mistakes, and approve better replies. Test the changed agent on similar calls and check for new failures.
Steps
- 01
Sample real calls and exports
Choose a set of live or staging calls. Keep the transcripts, tool results, and outcomes where available.
Choose recent calls for one task. Remove unnecessary personal details and keep a call ID. Include calls handled by the agent and calls handed to a person.
- 02
Write a voice success rubric
Define what must pass for the whole conversation, including actions as well as the final reply.
Include required confirmations, disclosures, tool checks, interruption recovery, and escalation triggers. Separate answer quality from conversation mechanics so reviewers score consistently.
- 03
Score trajectories and failure clusters
Group failures by intent, ASR/entity error, tool break, policy miss, or handoff gap.
Start with mistakes that recur or can cause harm. Include variations of those cases so you can check whether a fix works beyond one call.
- 04
Capture the approved utterance or path
Have a reviewer write what the agent should have said or done, plus a short critique.
Include confirmation language for uncertain ASR, safe refusals, and handoff summaries. Mark approval status and policy scope so the correction can be reused or retired.
- 05
Re-test and measure
Apply the prompt change, tool rule, or retrieved examples and compare results on the same type of call.
Track resolved calls, handoffs, time spent on edits, and response time. Check other request types for new failures before expanding the change.
Common mistakes in voice AI testing
- Testing only scripted happy paths and calling it coverage
- Grading transcripts while ignoring barge-in, silence, and latency
- Failing calls without writing an approved replacement utterance
- Rolling retrieval or prompt changes live without a held-out comparison
Example voice test record
Observed failure
The agent says the request is complete before the tool returns a result, then talks over the caller's correction.
Approved correction
Acknowledge the wait, confirm only after the tool result, repeat the key detail, and offer a clear next step or escalation.
Controls
Tag as voice support, refill status, approved; restrict retrieval to matching workflow and disclosure rules.
Run this as a service engagement via voice AI testing servicesStore approved call corrections in golden datasets for AI agents
What to record and test
Save the call details, review each step, approve the correction, and test the change.
Instrumentation
Retain call or trace ID, turns, tool status, latency, disclosures, and outcome labels.
Scenario matrix
Cover barge-in, silence, ASR uncertainty, entity confirmation, tools, disclosures, and handoffs.
Choose the calls
Start with recent, difficult calls for one task. Include variations of recurring mistakes and expand once reviewers apply the scoring rules consistently.
Thresholds
Define pass/fail for confirmation, disclosure, latency, and escalation before scoring begins.
Real calls and simulations
Use simulations to test extra scenarios. Include real call replays to check speech, noise, and actual tool results.
Sample scored call
Use the print-ready checklist; capture observed failure, approved correction, and release decision.
Free template
Use the print-ready voice AI test checklist
Cover ASR uncertainty, entity confirmation, barge-in, silence, latency, tools, disclosures, handoffs, and outcomes.
This template uses made-up examples, not client data. Adapt it to your policies, privacy requirements, and approval process.
How less than three tests voice agents
The Custom Model Sprint applies this loop to one voice workflow: audit calls, approve conversational replies, test the agreed change, and measure task completion and escalation quality. Language-model fine-tuning is separate from voice cloning or TTS training.
Limitations
- Happy-path scripts under-test live call risk.
- Transcript grading alone misses interruption and latency failures.
- Failed calls without approved replacements do not teach the next turn.
- Rolling retrieval live without a held-out comparison creates uncontrolled risk.
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:
Related
Want this done on your calls?
Book a call to discuss the task and tests you need. We'll agree data handling before you share recordings or transcripts.