How to Test an AI Agent: 7 Powerful Tips

7 Important Areas to Test in an AI Agent

1. Test Whether the Agent Understands the User’s Request

The first step is to verify whether the agent correctly understands the user’s goal.

For example, a user says:

“Find a doctor near me and book an appointment for tomorrow morning.”

The agent should understand that it needs to find a suitable doctor and arrange an appointment. However, it should not assume missing details or make a booking without the required confirmation.

Test scenarios:

• Give a clear and simple instruction.
• Use different wording for the same request.
• Provide incomplete information.
• Give an ambiguous instruction.
• Include contradictory details.
• Ask the agent to perform an unsupported task.

Expected result: The agent should understand the request correctly, ask relevant follow-up questions when necessary, and avoid taking actions based on unsupported assumptions.

2. Test Whether the Agent Selects the Correct Tool

AI agents may use tools such as APIs, databases, search services, or email systems.

For example, if the user asks to check appointment availability, the agent should use the appropriate availability-checking tool instead of creating a booking immediately.

Test scenarios:

• Verify that the correct tool is selected.
• Check whether the tool receives the correct parameters.
• Provide invalid or incomplete parameters.
• Make a required tool unavailable.
• Test whether the agent handles tool errors correctly.
• Verify that the agent does not call unnecessary tools.

Expected result: The agent should select the appropriate tool, pass valid information, and handle failures without pretending the task succeeded.

3. Test Whether the Agent Performs the Correct Action

This is one of the most important areas of AI agent testing.

An agent may understand a request correctly but still perform the wrong action.

For example, the user asks:

“Update my phone number to 9876543210.”

The agent should update the correct user’s phone number and preserve other profile information.

Test scenarios:

• Update a valid record.
• Attempt to update a nonexistent record.
• Submit invalid data.
• Try updating a field the user is not allowed to change.
• Verify that unrelated fields remain unchanged.
• Check whether repeated requests create duplicate changes.

Expected result: Only the intended change should occur, and the system should maintain data accuracy and integrity.

4. Test Permissions and Security

An AI agent should not be allowed to perform every action simply because a user requests it.

For example, an agent working with customer records should not expose confidential information or modify records without the necessary authorization.

Test scenarios:

• Ask the agent to access restricted information.
• Attempt to modify another user’s record.
• Request an action without the required permission.
• Ask the agent to reveal API keys or passwords.
• Include malicious instructions in a document the agent reads.
• Try to bypass approval requirements through prompt manipulation.

Expected result: The agent should follow the application’s access controls and security policies, reject unauthorized actions, and avoid exposing sensitive information.

For more guidance on AI security risks, explore the OWASP GenAI Security Project, which provides resources for understanding and addressing security challenges in generative AI applications.

Remember: security must be enforced by the underlying application and tools, not just by asking the AI to behave safely.

5. Test Confirmation Before High-Risk Actions

Some actions can cause serious problems if performed incorrectly.

Examples include:

• Sending emails to external recipients.
• Deleting important records.
• Making payments.
• Cancelling appointments.
• Publishing content publicly.
• Changing access permissions.

A well-designed agent should follow the required approval process before executing sensitive or irreversible actions.

Test scenarios:

• Request an action that requires confirmation.
• Refuse to confirm the proposed action.
• Change the action details before confirmation.
• Attempt to reuse an old confirmation.
• Test whether the agent executes an action twice after receiving confirmation.

Expected result: The agent should follow the configured approval rules and execute only the authorized action with the correct details.

Not every action requires confirmation. The expected behavior should match the application’s risk policy and requirements.

6. Test Error Handling and Recovery

What happens if an API fails or the internet connection is lost while the agent is performing a task?

A reliable AI agent should handle such situations safely.

Test scenarios:

• Simulate an API timeout.
• Return a server error.
• Provide an unexpected API response.
• Interrupt a multi-step workflow.
• Make a tool return incomplete information.
• Retry an action after an uncertain result.
• Test recovery after a temporary failure.

Expected result: The agent should communicate failures clearly, recover when possible, and avoid duplicate or unintended actions.

For example, if a booking API times out after creating an appointment, the agent should verify the booking status before trying again. Otherwise, the user might receive two appointments.

7. Test the Complete Workflow from Start to Finish

An AI agent may need several steps to complete one request. Even if each individual step works correctly, the complete workflow can still fail.

Consider this request:

“Find an available doctor, book an appointment, and send me the confirmation.”

The workflow may involve:

1. Understanding the request.
2. Searching for doctors.
3. Checking appointment slots.
4. Confirming the selected slot when required.
5. Creating the booking.
6. Verifying the booking result.
7. Sending the confirmation.

Test scenarios:

• Complete the workflow successfully.
• Fail one step in the middle.
• Return incorrect information from one tool.
• Interrupt the workflow and resume it.
• Verify that the confirmation matches the actual booking.
• Check whether the agent stops when a required step fails.

Expected result: The agent should complete the requested workflow accurately, maintain consistency between steps, and report the actual outcome.

Pages: 1 2 3 4 5 6