How to Test a Sarvam AI Voice Agent: A Complete Step-by-Step Guide

Sarvam AI provides an integrated platform to build and deploy conversational voice agents, combining speech recognition, language models, voice synthesis, and business integrations in a single environment. Through its Voice Agents platform, developers can configure agent instructions, manage customer information, connect external tools, and support conversations across multiple languages.

Testing these agents requires examining how the entire conversation unfolds, not simply whether the agent generates an appropriate response. A customer may change their request during an interaction, provide incomplete information, switch languages, or trigger a backend operation that fails unexpectedly. Each situation can affect whether the agent successfully completes its intended business task.

Sarvam AI includes native testing capabilities for evaluating these behaviors. Its Voice Agents workspace supports manual voice and chat testing, automated conversation simulations, AI Judge evaluations, repeated test execution, and API-based testing workflows. These methods let teams examine conversation logic, validate expected outcomes, and catch inconsistencies before deployment.

This guide explains how to test a Sarvam AI voice agent step by step, covering agent configuration, manual testing, simulation scenarios, automated evaluations, multilingual speech validation, external tool execution, regression testing, and production monitoring.

How Do You Test a Sarvam AI Voice Agent?

You can test a Sarvam AI voice agent in the Voice Agents workspace through interactive conversations, automated simulations, and production call analysis.

Sarvam offers several testing methods, each designed to examine a different part of the agent's behavior.

The most effective testing process begins with manual conversations, progresses to reusable simulation scenarios, and then validates real voice conditions and external integrations.

For production deployments, teams should also repeat key tests after agent changes and investigate real conversations through available monitoring tools.

Step 1: Open Your Sarvam AI Voice Agent in the Canvas

Before running tests, confirm that your agent is configured correctly in the Sarvam Voice Agents workspace.

Sarvam's Canvas provides a central interface for managing the agent's instructions, variables, tools, settings, and tests.

How to access your agent

1. Sign in to the Sarvam AI platform.
2. Open Voice Agents from the navigation menu.
3. Select the agent you want to test.
4. Open its Canvas.
5. Review the Instruction, Variables, Tools, Settings, and Tests tabs.

Start by reviewing the agent's instructions and opening greeting.

Confirm that the agent's business responsibilities, supported languages, connected tools, and fallback behavior match the intended application.

For example, if the agent handles appointment scheduling, the instructions should clearly explain what information to collect and when to confirm a booking.

Check the configured variables and tool connections before starting tests. Incorrect configuration can cause failures unrelated to the agent's conversational ability.

Step 2: Test Conversations Manually Using Chat and Voice

Sarvam AI provides an interactive testing panel that allows developers to communicate with an agent before deployment.

The testing interface supports Voice, Chat, and Phone channels, although Phone testing is available only for eligible or allowlisted numbers.

How to run a manual test

1. Open your agent's Canvas.
2. Click Test Agent.
3. Select Chat or Voice.
4. Open Setup to configure the test session.
5. Choose the supported language and speaker, where applicable.
6. Override agent variables with test values if necessary.
7. Click Start Call to begin.
8. Interact with the agent and review its behavior.

Chat testing is especially useful for testing conversation instructions without adding speech recognition.

Voice testing adds real-time audio behavior, making it useful for evaluating how the agent understands spoken requests and generates responses.

Example: Testing a customer support agent

Consider a voice agent that handles delivery inquiries.

A customer asks:

"I'd like to know when my order is expected to arrive."

The agent should identify the tracking request and ask for the information required to retrieve the shipment.

Now introduce a change:

"Actually, I need to update the delivery address first."

The agent should recognize that the customer's objective has changed and follow the appropriate workflow.

It should not continue with shipment tracking if the customer has explicitly requested a different action.

Step 3: Create Automated Test Cases in the Tests Tab

Manual conversations help you explore agent behavior, but they're hard to repeat consistently.

Sarvam AI's Tests feature lets developers create reusable scenarios that run automatically.

Each test case includes a simulated user scenario and expected behaviors.

How to create a test case

1. Open the agent's Tests tab.
2. Click Add test.
3. Enter a descriptive name.
4. Write the simulated user's scenario.
5. Add the expected behaviors.
6. Save the test case.

If the agent doesn't yet have tests, Sarvam also offers an option to generate a starter set using AI. Review and refine generated tests before relying on them.

Example: Testing appointment rescheduling

Test name: Reschedule Appointment After a Change in Preference

User scenario:

A customer has an existing appointment and wants to move it to another day. During the conversation, the customer changes their preferred time and asks whether an earlier appointment is available.

Expected behaviors:

• The agent identifies the customer's final requested date and time.
• It checks availability before confirming.
• It does not confirm a slot that is unavailable.
• It communicates the final outcome clearly.

This test evaluates whether the agent can handle changing customer preferences while following the required scheduling process.

How to write effective scenarios

Describe the caller's objective and behavior rather than scripting every sentence they should say.

For example, a useful scenario might describe a customer who is in a hurry and challenges a cancellation fee.

This gives the simulated user room to express the request naturally while keeping the test focused on a specific objective.

Avoid combining unrelated tasks into one scenario. Separate tests make failures easier to diagnose.

Related: how to write voice agent test cases that catch failures.

Step 4: Define Expected Behaviors for AI Judge Evaluation

Sarvam uses an AI Judge to evaluate simulated conversations against predefined expectations.

Each expected behavior receives a Pass or Fail result with an explanation.

This makes the quality of the evaluation criteria especially important.

A vague condition such as "the agent handled the customer correctly" is difficult to assess consistently.

A specific condition such as "the agent repeats the final appointment date and time before ending the conversation" is more useful.

Example: Testing a customer verification rule

Consider an account support assistant.

The simulated customer requests protected account information but cannot complete the required identity verification.

Define the expected behaviors:

Behavior 1: The agent requests the required verification information.

Behavior 2: The agent does not disclose protected account information before verification.

Behavior 3: The agent explains an approved alternative when verification cannot be completed.

The AI Judge evaluates whether these behaviors occurred during the simulated conversation.

Interpreting the results

Review both the overall test outcome and the individual behavior evaluations.

A test that passes several conditions but fails one important authorization requirement should not be considered successful.

The judge evaluates evidence available in the conversation. It cannot independently confirm whether a backend operation succeeded unless the test's observable results include that evidence.

Step 5: Run Simulations and Review the Results

Once you create test cases, Sarvam AI can automatically run simulated conversations.

A simulated user interacts with the agent according to the scenario, and the AI Judge evaluates the resulting conversation.

How to run a simulation

1. Open the Tests tab.
2. Choose an individual test and select Run, or click Run all for the full suite.
3. Confirm the agent version selected in the Canvas.
4. Choose the number of executions per test case.
5. Optionally provide a name for the run.
6. Start the test.
7. Open the completed results.

Sarvam runs each test against the agent version currently open in the Canvas.

A test case is considered successful when all its expected behaviors pass.

Example: Evaluating a failed booking scenario

Suppose the simulated customer requests an appointment outside the business's operating hours.

The agent should explain that the requested time is unavailable and offer valid alternatives.

If the agent confirms the unavailable appointment, the test should fail.

Open the failed execution and inspect the conversation transcript to determine why the agent made the incorrect decision.

Possible causes include incomplete instructions, inaccurate knowledge, missing tool validation, or an evaluation requirement that needs refinement.

Step 6: Repeat Test Cases to Measure Consistency

A voice agent may behave correctly during one simulation and produce a different result in another.

Sarvam supports repeated execution to help identify these inconsistencies.

In the dashboard, you can run each test case 1, 2, 3, or 5 times.

How to run repeated simulations

1. Open the Tests tab.
2. Select the test or suite to execute.
3. Choose the execution frequency.
4. Start the run.
5. Review the pass count and failed executions.
6. Compare the individual transcripts.

For example, suppose an account verification test runs five times.

The agent follows the verification requirement in four conversations but discloses sensitive information in the fifth.

That single failure represents a serious issue, even though the test achieved an 80% pass rate.

Repeated testing is especially important for authorization, account updates, financial operations, and other business-critical interactions.

Step 7: Test Multilingual Speech and Real-World Audio

Sarvam's Voice Agents platform is designed primarily for Indian-language voice applications, with multilingual and code-mixed speech capabilities.

For global engineering teams evaluating Sarvam, testing should focus on the specific deployment's supported languages and regions rather than assuming universal language coverage.

Related: voice agent testing for NBFCs in India.

A simulated conversation may show that an agent follows the right business instructions but doesn't show how well it understands different speakers over a microphone or phone connection.

How to run real-world voice tests

1. Open the agent's testing panel.
2. Choose Voice.
3. Configure the relevant language and speaker settings.
4. Begin a conversation using a microphone.
5. Introduce natural pronunciation and speaking variations.
6. Repeat important scenarios with representative speakers.
7. Review the results and any available session records.

Example: Testing spoken appointment details

A customer asks:

"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 request, interpret the intended date and time, check availability, and confirm.

Repeat this test with speakers who have different accents and speaking speeds.

Also test callers who pause, correct themselves, or switch between configured languages.

Step 8: Verify External Tools and Business Outcomes

Voice agents often connect to external business systems such as customer relationship management platforms, booking services, and payment processors.

A conversation may appear successful even when the requested backend operation has failed.

For example, a customer may ask:

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

The agent might acknowledge the request and report that the address was updated.

However, that confirmation doesn't confirm whether the correct customer record was updated.

How to test tool execution

First, use a controlled environment or synthetic customer information.

Then initiate a conversation requiring the agent to call the relevant external tool.

Inspect the information provided to the tool and verify the operation's actual result in the connected backend.

Repeat the test with invalid information, missing fields, unsuccessful responses, and service timeouts.

For sensitive operations, confirm that the tool enforces identity verification and authorization before running.

Example: Testing a failed update

Suppose a CRM rejects an address change because the customer ID is invalid.

The agent should explain that the update could not be completed or follow the configured recovery procedure.

It should not claim that the customer record was updated successfully.

Step 9: Automate Sarvam Voice Agent Tests Using the Tests API

Sarvam provides a Tests API to manage test suites, run simulations, and programmatically retrieve evaluation results.

This can help engineering teams incorporate agent testing into their release workflows.

Unlike the dashboard, the API executes tests against committed agent versions.

How the testing API works

The general workflow is to create or select a test suite, configure its test cases and variables, run the suite against a committed agent version, and retrieve the resulting evaluations.

The API supports suite-level variables, test-specific overrides, global guardrails, and version-specific execution.

Global guardrails can represent requirements that apply across an entire suite, such as preventing unauthorized disclosure of protected information.

Example: Running a test suite using Python

import os
import requests

org_id = os.environ["SARVAM_ORG_ID"]
workspace_id = os.environ["SARVAM_WORKSPACE_ID"]
suite_id = os.environ["SARVAM_TEST_SUITE_ID"]
api_key = os.environ["SARVAM_API_KEY"]

url = (
    f"https://apps.sarvam.ai/api/evals/v1/"
    f"{org_id}/{workspace_id}/test-suites/{suite_id}/runs"
)

payload = {
    "app_id": "your-agent-id",
    "app_version": 4,
    "frequency": 2,
    "run_name": "Pre-release regression test",
    "on_end_eval": True
}

response = requests.post(
    url,
    headers={
        "X-API-Key": api_key,
        "Content-Type": "application/json"
    },
    json=payload,
    timeout=30
)

response.raise_for_status()
print(response.json())

Replace the sample agent identifier and version with the actual committed version being tested.

The API returns a run identifier when it accepts the request. The run may still be executing, so retrieve its status until it completes before interpreting the results.

You can use the results to identify failed test cases, inspect transcripts, and compare quality across releases.

Step 10: Monitor Production Calls and Convert Failures into Regression Tests

Pre-deployment tests help identify known risks, but real customer conversations can reveal behavior not anticipated during development.

Sarvam provides monitoring features for examining call activity, conversation outcomes, and operational performance.

After deployment, teams should review failed or incomplete calls and investigate whether the underlying problem relates to conversation logic, speech recognition, external integrations, or telephony behavior.

Example: Investigating a failed account update

A customer calls to change their contact information.

The agent follows the expected conversation flow, collects the necessary details, and confirms the update is complete.

Later, the customer reports that their account information has not changed.

Review the available call record, transcript, and backend execution evidence.

The failure may have resulted from an incorrect account identifier, an unsuccessful API operation, or an inaccurate confirmation.

After identifying the cause, create a regression scenario that reproduces the relevant conditions using synthetic or appropriately de-identified data.

Include that scenario in future simulation runs.

Building a continuous testing process

Maintain a reusable suite of tests covering the most important business workflows and previously observed failures.

After updating agent instructions, tools, knowledge, or language settings, rerun the relevant scenarios before deploying the updated committed version.

Compare the results with the previous baseline, investigate regressions, and validate any changes affecting real audio through representative voice tests.

What Metrics Should You Track When Testing Sarvam AI Voice Agents?

Testing should produce measurable evidence of both conversational quality and business reliability.

Review these measurements across scenarios, agent versions, supported languages, and relevant calling conditions.

A strong aggregate simulation pass rate may hide poor performance in a specific business workflow or with a particular type of spoken information.

Individual test results and representative production conversations are therefore essential for understanding overall quality.

How Vattara AI Can Complement Sarvam AI Voice Agent Testing

Sarvam AI provides native capabilities for manual testing, simulated conversations, AI Judge evaluation, version-specific execution, and production monitoring.

These tools give development teams a foundation for validating conversational behavior within the Sarvam ecosystem.

For organizations managing complex voice AI deployments, quality assurance may also involve broader evaluation of speech performance, business integrations, regression scenarios, and real-world operational behavior.

Vattara AI focuses on voice AI testing, evaluation, monitoring, and observability across development and production.

A complementary quality assurance workflow can help teams organize representative customer scenarios, evaluate interactions against defined quality criteria, investigate failures, and maintain consistent regression testing practices.

The objective is to connect the agent's communication to the overall reliability of the voice workflow.

Sarvam AI Native Testing vs. a Dedicated Voice AI Testing Workflow

The appropriate combination depends on the deployment architecture, required coverage, and supported integrations.

Teams should validate compatibility before implementing an automated workflow between Sarvam and a third-party testing platform.

Related: how to test a Retell AI voice agent.

Common Mistakes to Avoid When Testing Sarvam AI Voice Agents

Using Vague Evaluation Criteria

Conditions like "the conversation went well" are hard for an AI Judge to evaluate consistently.

Define observable requirements, such as confirming the requested date, completing required verification, or avoiding unsupported transactions.

Testing Only One Conversation

A single successful simulation can't prove an agent behaves consistently.

Repeat important scenarios and investigate occasional failures, particularly when they affect authorization or irreversible operations.

Treating Simulated Conversations as Real Audio Tests

Automated simulations help evaluate conversation logic but do not fully validate speech recognition, microphone quality, background noise, or telephone network behavior.

Test those conditions separately through real voice interactions.

Ignoring Language-Specific Differences

An agent may perform reliably in one configured language but struggle with another.

Test each supported language and relevant language-switching scenario using representative speakers.

Relying on Verbal Confirmation Instead of Backend Evidence

An agent may state that an appointment or account update succeeded even when the external operation failed.

Verify the outcome in the connected business system.

Conclusion

Sarvam AI provides an integrated environment for testing conversational agents through interactive conversations, automated simulations, AI-based evaluation, and production monitoring.

Manual tests help developers investigate how conversations behave. Simulated test cases and AI Judge evaluations make business requirements measurable, while repeated execution helps identify inconsistent outcomes.

Real voice testing, backend verification, and production analysis extend these checks to the complete customer experience.

By combining these methods with reusable regression tests and clear acceptance criteria, teams can catch failures earlier and keep voice agents reliable as applications evolve.

Frequently Asked Questions

Sarvam AI is a trademark of its respective owner. Vattara AI is not affiliated with or endorsed by Sarvam AI. Product capabilities described here reflect Sarvam’s public documentation at the time of writing; verify features for your plan and deployment.