AI Agent Testing: Stop Caveman Testing and Use Evals
I recently gave a talk at the LangChain Miami meetup about evals. This blog encapsulates the main points of the talk.
AI agent testing is one of the biggest challenges in building reliable LLM applications. Unlike traditional software, AI agents have infinite possible inputs and outputs, making manual testing inefficient and incomplete. This guide covers practical AI agent evaluation strategies that will help you move from manual testing to automated evaluation frameworks.
I build AI agents for work, and for a long time, I was iterating on them the worst way possible.
The test-adjust-test-adjust loop is how you improve agents. You try something, see if it works, tweak it, try again. Repeat until it's good enough to ship. The problem isn't the loop itself—it's how slow and painful that loop can be if you're doing it manually.