Your AI Eval Has a Blind Spot. You Built It.

The people who know your AI agent best may be the people least able to see all of its flaws. Not because they are bad engineers. Because they built it. Years ago, when I was taking art classes, my teacher told me something I've never forgotten: “Sara, you can't judge your own art.” I remember thinking, of course I can. 😂 Then she explained. After spending hours looking at the same piece, your eyes get filled with it. You stop seeing what is actually there. You see what you expect to see. I've used that lesson everywhere since. And I think AI agents have the same problem. You designed the requirements. You designed the system. You know why every decision was made. Then you design the evaluation and ask: “Does my agent actually work?” That's where the blind spot can appear. Your evaluation may end up testing the system according to the same assumptions that created it. The evaluator can inherit the system's assumptions Consider a simple requirement: “The agent should answer customer questions accurately.” Seems reasonable. So the team creates an evaluation set with questions that have clear intent and well-defined answers. The agent performs beautifully. 94%. Green dashboard. 🎉 But an external evaluator might ask a different question: What happens when the customer's request has two plausible interpretations? Now you have a different test: “Can I change my billing address?” Does the agent answer immediately? Does it ask which account or address the customer means? Does it make an assumption? The original evaluation may have been technically correct. It just never tested the ambiguity. That is the blind spot. Internal evaluation is still essential This isn't an argument that internal teams shouldn't evaluate their own systems. They absolutely should. The people who built the system understand its requirements, architecture, constraints, tools, and intended behavior better than anyone. That knowledge is extremely valuable when designing evaluations. But it can also crea