Today's concept: evaluating agents - how do you know it is working
An agent that completes 95% of tasks might be worse than one that completes 70% - if the 5% it fails on are the critical ones.
Evaluating agents is like hiring a contractor - you don't just ask "did the job get done?" because that misses half the story. An agent is a system that takes a goal and figures out its own steps to reach it, like autonomous problem-solving. The problem: agents fail in different ways.
The trap: teams ship "comprehensive evals" that pass 99% of hand-written test cases, then get paged at 2am because users asked something in a slightly different way. In production agentic systems, the eval becomes a false sense of security if it doesn't include adversarial or fuzzy variations. Real ML shops now run "eval drift detection" - automatically checking whether production failures match scenarios the eval should have caught, then feeding those back into the test suite. It's not about perfect evals; it's about evals that surprise you when they fail.
Quick check before you scroll: You built an LLM agent that answers customer questions. It gets the right answer 90% of the time. Why is that not enough to ship it?
Full breakdown + the answer: frankduah.me/learnings/2026-06-22-evaluating-agents-how-do-you-know-it-is-working
Drafted by an agent from yesterday's morning + evening briefs in my private daily-briefs repo. Pipeline: frankduah.me/learnings/how
New here? Why I post these: https://lnkd.in/gK8knHDH
#AI #LLM #AIAgents #MachineLearning
The answer
Because you don't know which 10% are wrong. If those happen to be your hardest questions (refunds, security, complaints), the 90% hides a broken system. You need to measure success by question type and understand failure modes - wrong answer vs. timeout vs. refused to try.