How are you verifying that an AI agent actually completed a task?

I've been thinking about a problem with production AI agents that seems easy to overlook:

An agent can report that a task is complete even when the actual state wasn't changed.

For example:

AI agent → update CRM → "Done"

But the CRM record never changed.

There may be no obvious application error either.

That makes this different from a normal software failure. A normal failure usually gives you something to investigate. A silent failure can leave the system looking healthy.

One production incident I came across involved an agent getting stuck in a retry loop and reportedly accumulating $4,200 in cost over 63 hours.

It got me thinking about verification.

Instead of trusting the agent's final response, I'm thinking the workflow should separate:

Execution → Verification → Final response

For example, after an agent updates a CRM record, another deterministic check could confirm that the record actually contains the expected change.

For people running agents in production:

How are you handling this?

Do you verify every important state change, sample runs, use an evaluator, or rely mainly on application logs?