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.