How to test an AI agent before deploying it

An AI agent that works in a demo is not necessarily ready for business use. What happens when it receives an unexpected request, cannot find the information it needs or encounters an integration error?

Before deploying an agent, you need to check how it handles real situations, which actions it performs and when it should ask for help. Testing an AI agent is about more than checking whether its answers sound correct. It is about making sure it can complete its tasks reliably and within defined boundaries.

This guide covers the key checks to run before giving users access to an agent or connecting it to business processes.

Define what the agent should do

Before testing begins, establish the agent's purpose and the outcome it is expected to deliver. An agent that answers order-related questions does not need the same permissions or criteria as one that updates CRM records.

Define the tasks it can perform, the information it can access and the actions it must not take. Clear boundaries make it easier to identify incorrect behaviour.

For example, a customer service agent might check an order status and explain delivery times, but it should not promise compensation if it has no authority to offer it.

Test real-world scenarios and exceptions

Testing should go beyond ideal situations. Alongside common requests, include cases that could lead to confusion or errors:

Missing information: What happens when the user leaves out a required detail?
Ambiguous instructions: Does the agent ask for clarification or make an unsupported assumption?
Unavailable data: Does it acknowledge that it cannot find the information?
Out-of-scope requests: Can it recognise when to decline or escalate a request?
Unexpected results: How does it respond when a tool returns an error?

Build a set of repeatable tests that you can run whenever you change the instructions, model or workflow. This helps you check whether an improvement fixes an issue without introducing new ones.

Check tools and integrations

An agent can produce a convincing answer and still perform a task incorrectly. That is why you need to verify what happens behind the conversation.

If it queries a database, check that it retrieves the right records. If it updates a CRM, confirm that it changes the correct field. If it sends a notification, verify the recipient and message.

For actions with real-world consequences, start with test data or controlled environments. Check both the expected result and how the system handles execution failures.

It is also important to ensure that the agent only uses the tools required for its role and does not have excessive permissions.

Decide when human help is needed

Not every situation should be handled automatically. A reliable agent should recognise its own limits.

You can define rules that require it to request more information when details are missing, ask for approval before sensitive actions or hand a case over to a person when it cannot resolve it confidently.

For example, an agent might prepare a refund request but wait for approval before processing it. This combines AI speed with human oversight.

Review, refine and test again

Testing does not end once an agent passes its first set of checks. Changes to company data, instructions and processes can introduce new issues over time.

Review conversations and execution logs, identify recurring errors and refine the instructions or workflow. Then rerun relevant tests to confirm that the changes work as expected.

Keeping a record of test cases, expected outcomes and identified issues also makes it easier to compare versions and avoid starting from scratch after every update.

Testing AI agents with NappAI

NappAI helps you build and refine agents connected to workflows and business tools. Its AI assistant can create and modify workflows, test them with sample data and help fix errors during the process.

This makes it easier to review an agent's behaviour before using it in a live process and refine its configuration as requirements change. Depending on the use case, you can also introduce approval steps and require confirmation before certain actions are carried out.

The goal is not to build an agent that answers everything. It is to build one that knows what to do, when to do it and when to ask for help.

Ready to build AI agents for your business processes? [Explore NappAI] to see how you can create workflows and agents tailored to your needs.