@maven_hq: How to evaluate AI Agents. If you’re only evaluating the final output of your AI agent, there’s a very good chance your agent is… not great. Most people build agents with multiple steps. They plan. They retrieve data. They call tools. They write to memory. And then they evaluate it like a multiple-choice test: “Did it give the right answer? Yes or no.” That’s a terrible way to evaluate agentic systems. Because an agent can: • Get the right answer using the wrong tool • Pull the wrong data but sound confident • Say it updated a database when nothing actually changed • Work once and completely fail at scale What you should do instead is evaluate every step of the process. Did it retrieve the correct data? Did it choose the right tool for the job? Did it call that tool in the correct order? If it claims it wrote to a database or file, did the system actually end up in the expected state? When you evaluate outputs and trajectories and end state, debugging becomes obvious. You stop guessing why your agent failed. You can see exactly where it went wrong. If you want to learn how the best teams in the industry evaluate agents with concrete frameworks and real examples, there’s a lecture that breaks this down step by step. Highly recommend watching it if you’re building anything agentic. #ai #agents #llms #coding #maven

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Friday 12 December 2025 17:03:30 GMT