Agent Testing

Agent testing lets you verify conversational responses, tool usage, and full multi-turn outcomes before you deploy. Create tests from scratch or from existing conversations, then run them from the dashboard, CLI, or API.

The framework includes three complementary test types:

• Simulation Testing — Runs end-to-end, multi-turn conversations with a simulated user
• Next Reply (Scenario) Testing — Validates the agent's next response against success criteria
• Tool Call Testing — Ensures the agent calls the right tool with the right parameters

Transform real conversations into test cases when you find an interaction where the agent underperformed.

1. Open the conversation in call history
2. Click Create test from this conversation
3. Review the prefilled context, then define the expected behavior
4. Add the test to your suite to catch similar failures later

Simulation testing evaluates your agent across a full, multi-turn conversation with a simulated AI user. Unlike Next Reply tests, this type checks whether the complete interaction reaches your defined outcome.

Describe the user's context, intent, and behavior in natural language. The simulator uses this scenario to drive the conversation.

Example scenario:

"A tourist who is not fluent in English is trying to place an order at a restaurant."

Define the outcome that should count as a pass. This prompt is used to evaluate whether the full conversation succeeded.

Example success condition:

"The agent confirmed the order details, handled clarifying questions, and completed the order without misunderstandings."

Choose how long the simulation can run before stopping. Use a lower value for focused checks and a higher value for complex workflows.

• Minimum: 1
• Maximum: 50
• Default: 5

Execute the test and inspect the generated conversation transcript. Review the pass/fail result against your success condition, then iterate on your prompt, tools, or agent configuration.

You can refine simulation behavior in the test configuration panel:

• Environment: Select which environment to test against when your agent has multiple environments configured. If only one environment is available, this selector is hidden.
• Chat history: Start from a partial conversation instead of a blank state. This is useful for testing in-progress conversations and recovery behavior.
• Dynamic variables: Inject test-specific values into your agent variables (for example, user names or order IDs) without changing the base agent configuration.

Simulation tests support tool mocking so your agent can receive controlled responses during a run instead of calling live systems.

• Mock none: No tools are mocked.
• Mock all tools: Every mockable tool returns a mock response.
• Mock selected tools: Only tools you explicitly choose are mocked.

System tools and workflow tools are never mocked.

If a mocked tool is called and no matching mock response is found, choose one of these behaviors:

• Call real tool: Executes the real tool call.
• Finish with error: Returns an error response from the tool instead of calling the real tool.

The fallback setting appears only when at least one tool is mocked.

Next Reply (Scenario) testing evaluates only the agent's next message, not a full multi-turn outcome. Provide conversation history that leads up to the reply you want to evaluate, then score that reply against success criteria.

For full multi-turn outcomes, use Simulation Testing.

Provide the conversation history leading up to the reply you want to evaluate. This can be a single user message or multiple turns of context.

Example chat history:

User: "I'd like to cancel my subscription. I've been charged twice this month and I'm frustrated."

Describe in plain language what the agent's response should achieve. Be specific about the expected behavior, tone, and actions.

Example success criteria:

• The agent should acknowledge the customer's frustration with empathy
• The agent should offer to investigate the duplicate charge
• The agent should provide clear next steps for cancelation or resolution
• The agent should maintain a professional and helpful tone

Supply both success and failure examples to help the evaluator understand the nuances of your criteria.

Success example:

"I understand how frustrating duplicate charges can be. Let me look into this right away for you. I can see there were indeed two charges this month - I'll process a refund for the duplicate charge immediately. Would you still like to proceed with cancellation, or would you prefer to continue once this is resolved?"

Failure example:

"You need to contact billing department for refund issues. Your subscription will be cancelled."

Execute the test. An LLM evaluator compares the agent's next reply against your success criteria and examples to determine pass/fail status.

Tool call testing verifies that your agent correctly uses tools and passes the right parameters in specific situations. This is critical for actions like call transfers, data lookups, or external integrations.

Choose which tool you expect the agent to call in the given scenario (e.g., transfer_to_number, end_call, lookup_order).

Specify what data the agent should pass to the tool. You have three validation methods:

Exact Match
The parameter must exactly match your specified value.

Transfer number: +447771117777

Regex Pattern The parameter must match a specific pattern.

Order ID: ^ORD-[0-9]{8}$

LLM Evaluation An LLM evaluates if the parameter is semantically correct based on context.

Message: "Should be a polite message mentioning the connection"

When testing in development, use dynamic variable values that match those that would be actual values in production. Example: {{ customer_name }} or {{ order_id }}

Execute the test to ensure the agent calls the correct tool with proper parameters.

Tool call testing is essential for high-stakes scenarios:

• Emergency Transfers: Ensure medical emergencies always route to the correct number
• Data Security: Verify sensitive information is never passed to unauthorized tools
• Business Logic: Confirm order lookups use valid formats and authentication

Write tests for new behavior or known failures, run them while you iterate on prompts and configuration, then save once they pass.

Navigate to the Tests tab in your agent's interface. From there, you can run individual tests, select multiple tests from your library as a batch, or execute your entire suite with Run All Tests.

Integrate testing into your development pipeline:

elevenlabs agents test agent_7101k5zvyjhmfg983brhmhkd98n6

This enables:

• Automated testing on every code change
• Prevention of regressions before deployment
• Consistent agent behavior across environments

Create tests with Create test and execute them with Run tests on the agent.

from elevenlabs import ElevenLabs

elevenlabs = ElevenLabs()

invocation = elevenlabs.conversational_ai.agents.run_tests(
    agent_id="agent_7101k5zvyjhmfg983brhmhkd98n6",
    tests=[{"test_id": "<test-id>"}],
)

print(invocation)

import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";

const elevenlabs = new ElevenLabsClient();

const invocation = await elevenlabs.conversationalAi.agents.runTests("agent_7101k5zvyjhmfg983brhmhkd98n6", {
  tests: [{ testId: "<test-id>" }],
});

console.log(invocation);

Agent outputs can vary between runs. A single pass shows the agent can succeed; probabilistic testing shows how often it will by running the same test multiple times and reporting a pass rate.

When triggering a test from the dashboard, use the split-run control on the run button to choose how many times to execute it (for example 3×, 5×, or 15×). Each run is independent: the agent receives the same chat history, dynamic variables, and other inputs, but its response is generated fresh every time.

Multi-run works for individual tests, folders, and running the entire test suite attached to an agent. It's compatible with all three test types — Simulation, Next Reply (Scenario), and Tool Call — and is typically most useful for Simulation tests, where the larger surface area of a multi-turn conversation makes response variation more likely.

After a multi-run finishes, results are summarized as a pass rate (for example, 4/5 passed) with a colored badge:

• Green — 100% passed
• Amber — at least 80% passed
• Red — below 80%

Individual runs are then grouped by failure reason so you can see how the agent fails, not just that it fails. Instead of scrolling through five separate transcripts to spot what differed, you see clusters like "Correctly routed to billing (4 runs)" and "Hallucinated a support number (1 run)", each expandable to the underlying transcripts and evaluation rationale.

• Before shipping a change — Re-run attached tests probabilistically to confirm reliability hasn't dropped (for example, from 95% to 60%).
• Diagnosing flaky behavior — A single failure could be noise; a 1-in-5 failure with a clearly named failure bucket is a reproducible issue to fix.
• Tuning prompts and tools — Iterate on configuration and compare pass rates side by side, rather than relying on one-off runs.

Pass repeat_count (between 2 and 20) on the run-tests request to execute each test that many times. Setting repeat_count automatically enables failure bucketing on the response, so the returned invocation includes the per-bucket grouping and pass rate you'd see in the dashboard.

from elevenlabs import ElevenLabs

elevenlabs = ElevenLabs()

invocation = elevenlabs.conversational_ai.agents.run_tests(
    agent_id="<agent-id>",
    tests=[{"test_id": "<test-id>"}],
    repeat_count=5,
)

import { ElevenLabsClient } from "@elevenlabs/elevenlabs-js";

const elevenlabs = new ElevenLabsClient();

const invocation = await elevenlabs.conversationalAi.agents.runTests(
  "<agent-id>",
  {
    tests: [{ testId: "<test-id>" }],
    repeatCount: 5,
  },
);

Test that your agent maintains its defined personality, tone, and behavioral boundaries across diverse conversation scenarios and emotional contexts.

Create scenarios that test the agent's ability to maintain context, follow conditional logic, and handle state transitions across extended conversations.

Evaluate how your agent responds to attempts to override its instructions or extract sensitive system information through adversarial inputs.

Test how effectively your agent clarifies vague requests, handles conflicting information, and navigates situations where user intent is unclear.

• View CLI Documentation for automated testing setup
• Explore Tool Configuration to understand available tools
• Read the Prompting Guide for writing testable prompts