How to Test AI Agents (Before They Burn Your Budget)

Your agent passed your demo. It handled the five prompts you tested by hand. The stakeholders nodded. You shipped it. Then it burned $153 in 30 minutes on a Tuesday afternoon. The agent hit an ambiguous query, entered a reasoning loop, and called GPT-4 47 times trying to resolve a question it should have escalated after three attempts. Nobody noticed until the billing alert fired. This is not a hypothetical -- it's the most common failure pattern in production AI agents, and it's entirely preventable. The problem isn't that teams skip testing. It's that they apply traditional testing patterns to systems that break those patterns by design. AI agents are non-deterministic, multi-step, and capable of generating confident-sounding output that is completely wrong. You can't assert response == expected when the same input produces different valid outputs on every run. Here are five testing patterns that actually work for AI agents. Each one is framework-agnostic, includes copy-paste Python