Sarvam AI Voice Agent Testing Checklist: 90+ Essential Checks Before Deployment

Sarvam AI provides a platform to build conversational voice agents that can handle customer inquiries, schedule appointments, qualify leads, and support business workflows through natural speech interactions. Its Voice Agents platform combines speech recognition, conversational AI, voice synthesis, and external integrations to help businesses automate conversations across supported languages.

Testing these agents involves evaluating both conversational accuracy and the technical systems that support each interaction. An agent may follow its instructions correctly during a simulated conversation but struggle when a caller speaks with an unfamiliar accent, changes languages, interrupts a response, or provides incomplete information. Similarly, an agent may communicate a successful outcome even when an external API fails to complete the requested operation.

Sarvam AI provides native testing capabilities, including simulated conversations, AI-based evaluation, repeated test execution, and test suite management. These features help developers validate agent behavior, identify failures, and evaluate changes before deployment.

This Sarvam AI voice agent testing checklist covers 90+ checks across agent configuration, multilingual understanding, conversational accuracy, simulations, function execution, audio quality, security, and production monitoring.

What Should You Test Before Deploying a Sarvam AI Voice Agent?

Before deploying a Sarvam AI voice agent, verify that it understands intended customer requests, follows business instructions, completes authorized tasks, and maintains reliable conversations under realistic conditions.

The testing process should cover both the agent's decision-making and its ability to interact through actual voice channels.

Sarvam provides several methods for evaluating agent behavior.

These methods serve different purposes. A successful simulation shows the agent met the defined conversational requirements for that test. It does not automatically establish that speech recognition, telephony, or backend integrations will behave correctly in production.

1Sarvam AI Agent Configuration0 / 10

• The agent's role and business responsibilities are clearly defined.
• The opening greeting is appropriate for the intended customer audience.
• Required languages are supported and configured correctly.
• The selected voice suits the application and audience.
• Agent instructions describe permitted and prohibited actions.
• Required customer and workflow variables are populated correctly.
• External tools and business integrations are configured properly.
• Call termination and escalation conditions are defined.
• Fallback responses are configured for unsupported requests.
• Test and production environments use appropriate separate credentials.

2Multilingual Understanding and Language Handling0 / 10

• The agent understands customer requests in every configured language.
• The agent evaluates supported accents and pronunciation variations.
• The agent tests regional expressions relevant to the customer audience.
• The agent recognizes the same intent across different supported languages.
• It maintains conversational context when the caller switches languages.
• The agent interprets English product names and technical terms correctly in multilingual conversations.
• The agent avoids incorrectly translating names, identifiers, or reference numbers.
• It responds in the appropriate language according to configured preferences.
• The agent handles multiple language changes within a conversation.
• Unsupported languages trigger appropriate clarification or fallback behavior.

3Speech Recognition and Information Accuracy0 / 10

• Customer names are recognized accurately.
• Geographic locations and addresses are transcribed correctly.
• International telephone number formats are handled appropriately.
• Dates expressed in different formats are interpreted correctly.
• Currency amounts are understood in the intended context.
• The system recognizes numerical values spoken in different ways.
• The agent accurately captures account numbers, booking references, and identifiers.
• The agent confirms critical information before taking consequential actions.
• Ambiguous speech triggers clarification rather than guessing.
• Domain-specific vocabulary is recognized accurately.

4Conversation Quality and Business Instructions0 / 10

• The agent correctly identifies the caller's primary intent.
• It follows the defined business workflow.
• It asks relevant questions when information is missing.
• It retains necessary context across multiple conversation turns.
• It handles customers changing their requests.
• It avoids unsupported or fabricated information.
• It accurately communicates unsuccessful operations.
• It provides appropriate alternatives when it cannot complete a request.
• It respects mandatory business rules throughout the conversation.
• It closes conversations only after the appropriate outcome or next step is established.

5Sarvam AI Simulation Testing0 / 10

• Simulation test cases represent important customer journeys.
• Every test case has a clearly defined caller objective.
• Simulated caller instructions reflect realistic customer behavior.
• Expected behaviors are measurable and specific.
• Standard customer requests are covered.
• Negative scenarios include invalid or missing information.
• Complex multi-turn interactions are tested.
• Language-switching scenarios are included where relevant.
• Review failed simulations using their transcripts.
• Repeat critical test cases to detect inconsistent outcomes.

6AI Judge Evaluation and Test Reliability0 / 10

• Evaluation criteria describe observable agent behavior.
• Critical requirements are evaluated independently.
• AI judge results include meaningful pass/fail explanations.
• Failed behaviors are investigated individually.
• Test results are reviewed against the actual conversation transcript.
• Important scenarios are executed multiple times.
• Inconsistent results are identified and investigated.
• Tests run against the intended agent version.
• Evaluation criteria are updated when business requirements change.
• Critical authorization or privacy failures block deployment regardless of the overall pass rate.

7Tool Execution and Backend Integrations0 / 10

• The agent selects the correct tool for each business operation.
• Required function parameters are complete and accurate.
• Customer verification occurs before protected operations.
• Successful API responses are interpreted correctly.
• The system handles invalid or incomplete API responses appropriately.
• API timeouts trigger appropriate recovery behavior.
• Failed operations do not result in false success confirmations.
• The system prevents duplicate or unauthorized tool execution where necessary.
• Backend records confirm completed business actions.
• Test operations do not unintentionally modify production data.

8Real-Time Audio and Conversation Timing0 / 10

• The agent understands speech under representative background noise.
• Callers can interrupt the agent where interruption handling is supported.
• The agent stops or adjusts speech appropriately after an interruption.
• Short acknowledgments do not consistently trigger false interruptions.
• The agent handles natural pauses without premature responses.
• The agent understands corrections made during conversations.
• Generated speech is clear and understandable.
• Important names and technical terms are pronounced appropriately.
• Long conversations maintain acceptable response timing.
• The agent recovers appropriately from temporary audio disruptions.

9Telephony and Call Reliability0 / 10

• Inbound calls connect to the intended agent.
• Outbound calls follow the configured calling workflow.
• Call setup and termination behave correctly.
• Phone audio quality meets the application's requirements.
• The system handles busy, unanswered, and disconnected calls appropriately.
• Human transfers reach the intended destination where supported.
• Failed transfers trigger an appropriate fallback.
• Relevant call events are recorded accurately.
• Network interruptions are tested.
• Call behavior is validated across representative deployment environments.

10Security, Privacy, and Compliance0 / 10

• Required identity verification is enforced.
• The agent does not disclose customer information to unauthorized callers.
• Attempts to override agent instructions are tested.
• Untrusted external content cannot authorize prohibited actions.
• Backend integrations enforce access controls independently of the agent.
• Credentials and API secrets are protected.
• Personal information is handled according to applicable privacy requirements.
• Recording consent and retention settings are validated.
• Sensitive operations require appropriate authorization.
• High-risk actions include confirmation and recovery controls.

11Performance and Quality Metrics0 / 10

• Response latency meets defined application requirements.
• Time to first audio is measured consistently.
• Average and high-percentile latency are reviewed.
• Speech recognition performance is measured using representative audio.
• Tool execution latency is monitored.
• Task completion is measured against actual business outcomes.
• Track escalation and transfer success rates.
• Quality metrics are analyzed by supported language.
• Performance is compared across relevant agent versions.
• Meaningful performance regressions are investigated.

12Regression Testing and Production Monitoring0 / 10

• Critical customer scenarios are stored in reusable test suites.
• Tests are rerun after meaningful configuration changes.
• Test results are associated with the relevant agent version.
• Previously identified failures are added to regression coverage.
• Production conversation data is available for authorized investigation.
• Monitor task completion and failure trends.
• Important tool errors are detectable.
• Recurring production issues are converted into test cases.
• Rollback procedures are documented.
• Incident-response responsibilities are defined.

How to Use This Sarvam AI Voice Agent Testing Checklist

The checklist is most useful when applied progressively, beginning with interactive testing and moving toward automated scenarios, real audio, and production monitoring.

Step 1: Test the Agent Through the Sarvam Dashboard

Sign in to Sarvam AI and open the Voice Agents workspace.

Select the agent you want to evaluate and open its Canvas.

Select the Test Agent option to open the testing panel.

Related: how to test a Sarvam AI voice agent, step by step.

Sarvam provides Voice, Chat, and Phone testing channels. Phone testing may be restricted to eligible or allowlisted numbers.

Begin with a straightforward customer request.

For example, consider an appointment scheduling agent.

A customer says:

"I'd like to schedule an appointment for Thursday afternoon. Can you check the available time slots?"

The agent should identify the scheduling request, collect any missing information, check availability through the appropriate system, and communicate the correct result.

Repeat the test with different customer requests, incomplete information, and follow-up questions.

Step 2: Create Automated Simulation Test Cases

Open the agent's Tests tab in the Canvas.

Select Add test to create a new scenario. Sarvam also provides an option to generate starter test cases with AI.

Define the caller's circumstances, objective, and expected agent behaviors.

For example:

Test name: Appointment Rescheduling with a Change in Preference

User scenario: A customer wants to reschedule an existing appointment. During the conversation, the customer changes their preferred date and asks whether an earlier time is available.

Expected behaviors:

• The agent identifies the customer's final requested date.
• It checks availability before confirming the change.
• It does not confirm an unavailable appointment.
• It communicates the confirmed outcome clearly.

Each expected behavior should be specific enough for an evaluator to determine whether it occurred.

Step 3: Run Tests and Review AI Judge Evaluations

From the Tests tab, select an individual test or use Run all to execute the suite.

Sarvam's simulated user interacts with the agent according to the defined scenario.

An AI judge evaluates the resulting conversation against each expected behavior.

Review the completed test's transcript, individual pass/fail results, and explanations.

A test passes when all its expected behaviors pass.

For critical scenarios, inspect the underlying transcript to verify that the AI judge's assessment reflects the actual required behavior.

Step 4: Repeat Tests to Evaluate Consistency

Sarvam supports repeated test execution.

In the dashboard, choose 1, 2, 3, or 5 executions per test case.

For example, consider a customer support agent that must verify identity before sharing account information.

Run the same scenario five times.

If the agent follows the required process in four conversations but fails once, investigate the failed execution.

A high overall pass rate does not make an authorization failure acceptable.

Related: what 50 test calls showed about caller personas.

Repeated testing helps identify behaviors that work in some conversations but are inconsistent in others.

Step 5: Test Real-World Multilingual Voice Conversations

Use Sarvam AI's Voice testing interface to evaluate how agents handle natural speech across their configured languages.

Testing should include speakers with different accents, pronunciation patterns, speaking speeds, and language preferences.

For example, consider a customer calling to reschedule an appointment:

"I'd like to move my appointment to tomorrow at 10 AM. Can you confirm whether that time is available?"

The agent should identify the rescheduling intent, understand the requested date and time, check availability, and communicate the correct outcome.

Repeat the scenario with speakers using different accents, supported languages, and natural phrasing variations.

Pay particular attention to names, dates, addresses, numbers, and domain-specific terminology.

Related: voice agent testing for NBFCs in India.

Step 6: Validate External Tools and Business Outcomes

If the agent connects to a CRM, appointment platform, payment processor, or other backend system, use controlled test data and appropriate non-production environments.

Confirm that the agent passes accurate information to the intended system.

For example, a customer might request:

"Please update my delivery address to 245 Park Avenue, New York."

The agent should capture the address correctly, perform any required verification, and use the appropriate integration.

The result should be checked against the actual backend record.

A positive verbal confirmation from the agent is not sufficient evidence that the operation succeeded.

Step 7: Run Regression Tests Before Deployment

Whenever your team modifies agent instructions, voice settings, language configuration, or integrations, rerun the critical test suite.

Sarvam provides a Tests API for managing test suites, test cases, and execution results.

For version-specific validation, run tests against the committed agent version intended for deployment.

Compare new results with previous baselines and investigate any scenario that previously passed but now fails.

Prioritize failures involving customer data, financial operations, authorization, or other consequential business actions.

Related: discovery vs. regression testing for Voice AI agents.

Step 8: Monitor Production Conversations

Once the agent is deployed, review available monitoring and analytics.

Track whether calls connect successfully, customer requests are resolved, external operations complete, and conversations end appropriately.

Investigate failed interactions using available call records and transcripts.

When you identify a new failure pattern, create a representative test case using synthetic or appropriately de-identified customer information.

Add it to the regression suite so future changes can be evaluated against the same scenario.