AI voice-agent testing checklist before launch

An AI voice-agent testing checklist should cover the caller’s goal, understanding and corrections, speaking turns, answer accuracy, system actions and handoffs. Give each scenario a clear expected result, check what actually happened and repeat important cases after changes. Use text tests for conversation logic and real audio or phone calls for the parts the caller hears.

This is a practical starting point for a business evaluating a new agent or improving one it already has. My work includes AI voice-agent evaluation for Acolad, covering testing, analysis and recommendations for aviation and healthcare applications. The checklist below is general guidance, with fictional examples you can adapt to your own workflow.

Start with the job and a controlled test setup

Write down what a successful call should achieve. For example: “Collect the enquiry and send it to the designated staff member with the correct contact details.” A reviewer can check that. “Provide excellent service” needs to be translated into observable behaviour first.

Identify the actions the agent can actually perform. Does it answer questions, collect a request, create a record or transfer a call? Confirm the intended route when it cannot finish. The test plan should follow those capabilities.

Use fictional caller details and a designated test destination. Check how each connected action behaves in the test environment. A button labelled “test” is not enough evidence that it cannot affect live records.

Retell’s simulation documentation explains that unmocked functions can reach real endpoints and that MCP tools ignore its function mocks. The practical lesson is to verify the boundary of each test connection before running a scenario that could create a booking, message or record.

The AI voice-agent testing checklist

Use this table to plan coverage. Adapt the examples to the job you wrote down, and record an outcome for each relevant scenario.

Test area	What to check
Purpose and opening	The caller understands who they reached and what the agent can help with
Intent	The agent identifies the request when it is phrased in different ways
Details and corrections	Names, dates and contact details remain accurate after a correction
Speaking turns	Pauses and interruptions lead to a usable conversation
Response timing	The caller receives an appropriate response or progress explanation
Answer accuracy	Answers follow the approved information and the agent’s scope
System actions	Any claimed action has a matching result in the destination system
Human handoff	The agreed transfer or follow-up route works and is explained clearly
Failure recovery	Missing information and unavailable services produce the agreed fallback
Closing	The caller understands what happened and what to expect next

Check understanding through variations

Give the agent the same goal using different wording. Include a direct request, a longer explanation and a caller who starts with background information. The intended outcome should remain consistent.

Then change one important detail during the conversation. In a fictional appointment enquiry, say Tuesday and later correct it to Thursday. Check the subsequent questions, closing summary and any resulting record. A verbal acknowledgement alone does not establish that the correction was retained.

Use names, terms and speaking styles relevant to the intended callers. Keep a note of which variations you tested so someone reading the report can understand its coverage.

Listen for usable speaking turns

Try a pause in the middle of a sentence, a short acknowledgement while the agent is talking and a deliberate interruption to correct something. Listen for whether the interaction recovers naturally and preserves the request.

LiveKit’s turn-handling documentation describes mechanisms for detecting speech and managing interruptions. These are behaviours to exercise in audio, because the timing of speech is part of what you are assessing.

Write down the moment that caused difficulty. “The agent started its next question before I finished giving the address” is more useful feedback than “the conversation felt awkward.” Describe the evidence first; let the builder investigate the cause.

Review timing in context

Separate an ordinary reply from a response that requires a lookup. Decide how the agent should explain a wait and what should happen if the lookup takes too long. Test that behaviour deliberately.

If you report latency, define the measurement. For a conversational response, that might be the interval from the end of the caller’s speech to the first audible reply. A system lookup may have a different useful measurement. Keep the definition next to the result.

Review slower interactions as well as typical ones. An average can hide the exact call a reviewer needs to hear. Your acceptance target should reflect the task and caller experience; this checklist does not assign an arbitrary universal threshold.

Check answers and completed actions separately

Prepare questions with known answers from the approved material. Include a request the agent cannot answer from that material. Check how it responds when information is missing or the request falls outside its agreed scope.

For an agent connected to another system, inspect the action itself. If it says a request was recorded, check the record and its details. If the action failed, check that the spoken response reflects the failure and gives the agreed next step.

LiveKit’s testing guidance includes tool arguments, error handling and factual grounding among the behaviours to assess. Those checks help connect the conversation to what the system actually did.

Exercise the handoff and fallback routes

Ask to speak to a person. Check the agreed route under both available and unavailable conditions. A transfer, a callback request and a message to staff create different expectations for the caller.

For a callback workflow, inspect what the staff member receives and whether it contains enough context to act. For a transfer, test the actual destination and what happens if it cannot answer. Use designated test numbers and recipients.

Introduce a missing required detail or a controlled lookup failure. Check whether the agent asks a useful follow-up question, acknowledges the limitation or follows the documented fallback. Add the closing message to the same review: the caller should understand whether anything remains unresolved.

Choose the test method for the question

Text tests are useful for examining a sequence of questions and answers. Saved scenarios let a team repeat that sequence after an edit. Audio tests add the experience of hearing the agent and speaking over the same channel as a caller.

Retell’s overview separates these methods: text and simulations for conversation behaviour, browser calls for audio interaction, and phone calls for telephony checks such as carrier audio and transfers.

Use that distinction when reviewing a supplier’s evidence. Ask which path was tested and what it leaves to verify. A simulated successful action should be followed by an appropriate integration check before relying on the live workflow.

Keep a test record that leads to a decision

Use the same fields for every scenario. This fictional example shows the level of detail to aim for:

• Scenario: Caller corrects the date of an appointment enquiry.
• Version and channel: Record the agent version and whether this was text, browser audio or a phone call.
• Expected behaviour: The corrected date appears in the closing summary and test record.
• Observed behaviour: The summary uses the corrected date, but the saved record contains the original date.
• Outcome: Fail for the end-to-end scenario.
• Evidence: Add the relevant call timestamp and test record reference.
• Recommended next step: Investigate how the correction reaches the record, then repeat the scenario.

Mark a scenario “not tested” when the necessary access or evidence is missing. Keep technical errors distinct from a pass or fail judgement about the agent. This gives the person approving the next step a clearer picture of what is known.

Repeat important cases and review the remaining gaps

Choose coverage according to the agent’s tasks and the consequences of a mistake. Include common requests, variations, corrections and the routes used when something fails. Repeat important cases so the review does not depend entirely on one run.

Retell describes saved scenarios and success criteria as a repeatable regression suite, and recommends rerunning cases after prompt or flow changes. It also notes that simulated callers and graders use language models, making repeated runs useful when interpreting results. See its simulation testing guidance.

Before release, review unresolved findings with the person responsible for the service. Decide which require a change, which need more evidence and which fit the agreed operating limits. Keep the test record attached to the version being considered.

For a business planning its first agent, the AI phone receptionist overview explains the wider workflow. For an existing agent, bring the task, the current experience and any test findings to a free 30-minute call. We can work out whether the next useful step is evaluation, a focused refinement or a change to the process behind the call.