LLM output is non-deterministic — the same input can be right today and slightly off tomorrow. Clicking through a few examples by hand never tells you whether your last prompt tweak caused a regression. Evals fix that with a measurable, repeatable testing workflow.
The key move: split the ONE stochastic part (the model) from everything deterministic around it (tools, parsing, dispatch, the loop), then test each. The agent test pyramid: unit-test your tools, mock the model with scripted actions to test the loop offline, and use eval suites (deterministic checks + LLM-as-a-judge) for fuzzy output. Guardrails are the runtime half: approvals before destructive actions, least-privilege tools, and human-in-the-loop checkpoints.