Tools: Powerful Evals For AI Agents

As agents become more autonomous, evaluation becomes the hardest — and most important — part of building them.

Two runs with the same input can produce different outcomes — and both might look correct.

You’re not just evaluating answers, you’re evaluating behavior.

An agent that gives the right answer for the wrong reason is a future bug.

This is where trace-based evaluation becomes critical.

A “successful” task that violates constraints is a failed eval.

You need behavioral evaluation, not just output scoring.

The goal isn’t to remove humans — it’s to use them where they matter most.

Using one model to evaluate another is controversial — but powerful.

If you can’t measure agent behavior, you don’t control it.

Source: Dev.to