Testing AI Agents in Simulated Environments

Your AI agent can write code, call APIs, and chain together multi-step workflows. But can you prove it works—not once, but reliably, across hundreds of runs, against every edge case and failure mode it will encounter in production? To do this, you’ll need some serious testing.

However, testing AI agents can get tricky. You need to run exponentially more tests to account for unpredictable LLM behavior, and you want these tests to happen in realistic conditions that simulate the real-world behavior of the environments your agents interact with. At scale, this can easily lead to uncontrolled environment sprawl, high costs, and overloaded APIs.

In this guide, the experts at WireMock explain why environment simulation is key to effectively testing AI agents, and how you can build realistic simulations that enable you to improve agent behavior over time.

Topics covered:

• The impact of non-determinism on your testing requirements

• How to isolate agent behavior in your tests

• Enabling adversarial testing to identify model misbehavior

• Creating stable environments for large-scale benchmarking tests

• Using WireMock Cloud to scale your environment simulation