An AI agent can look like it is working perfectly. It receives a task, uses the right tools, and returns a result that looks correct.
But what if it just got lucky?
What if it used the wrong tool, made ten unnecessary API calls, or makes a completely different decision when given a similar task?
That is why evaluating AI agents is becoming less about asking “Does it work?” and more about asking “How well, consistently, and safely does it work?”
Evaluating AI agents means systematically testing not only the final result, but also the process that led to it. Modern evaluation approaches look at an agent’s decisions, tool calls, costs, safety, and behavior when things go wrong.