An agent is a workflow, not a promise
A language model can suggest an answer. An agent can also choose tools, inspect results and carry a task through several steps. Those capabilities are useful only when the goal, permitted actions and stopping conditions are clear.
Start with a narrow task whose result can be checked. Keep a human decision point wherever an error could expose data, change production or create a financial commitment.
Evaluate the outcome, not the confidence
An eloquent answer is not evidence of correct execution. Ask what was read, what changed, which tests ran and what remains unverified. A tool failure should produce a visible limitation, not a plausible replacement result.
Compare a proposed improvement against a repeatable baseline. Record both the quality of the outcome and the cost of reaching it: time, tool calls, failed attempts and human corrections.