What is agent testing?
Agent testing is how you verify that an AI agent does the right thing before it touches production. Unlike a chatbot, an agent takes actions, so testing has to cover more than the text it produces. It has to cover the decisions it makes, the tools it calls, the workflows it completes, and how it behaves when the real world pushes back.
Why it is different from testing a chatbot
A chatbot returns text, so you can grade the text and be done. An agent decides what to do and then does it. It sends a message, updates a record, or moves money. Once software acts on the world, correctness is not about the words it wrote, it is about the state it left behind. That shift is the whole reason agent testing is its own discipline.
What agent testing covers
- Reasoning: did the agent understand the task and plan a sensible approach
- Tool use: did it call the right tools with the right arguments in the right order
- Workflows: did it finish a multi-step process and keep state consistent along the way
- Robustness: did it handle errors, rejected actions, and slow responses without falling apart
- Safety: did it refuse unsafe requests and resist manipulation
Where to start
Most teams begin by evaluating the model's reasoning and its tool calls, using a dedicated eval framework (the tools guide compares the main ones). That is a good foundation. The gap that tends to bite later is testing the full workflow against systems that hold state and fail realistically, which is where a lot of production incidents originate. For a broader view of how agents are built and where they break, Building effective agents is a useful primer.