Human + Agent Learning Loop

Teams often discuss human review as a temporary control. The agent starts with a person checking the work, then the person is removed when accuracy reaches an acceptable level. I think that misses the more useful part of the design.

Human involvement should create a learning loop. When a person changes an answer, rejects an action, or handles an exception, that decision should tell the team something. Why was the agent wrong? Was the input incomplete? Was the policy unclear? Did the process depend on judgment that nobody had documented?

A review step without structured feedback becomes operating cost. People correct the same errors, but the system does not improve. A useful loop captures the reason for the correction and routes it to someone who can act on it.

The owner also needs to be clear about what the agent is allowed to do. Can it recommend, draft, decide, or execute? Which outcomes require approval? What happens when confidence is low? Who handles an escalation? Authority should expand because the evidence supports it, not because autonomy sounds like the destination.

The learning may improve more than the model. It can expose weak instructions, missing data, inconsistent policy, or disagreement inside the business. Sometimes the right fix is a better prompt. Sometimes it is a better process.

I would measure whether the loop is reducing repeated corrections, improving outcomes, and lowering the effort required from reviewers. If the same people keep fixing the same issues, the loop is not working yet.

The objective is not to remove people as quickly as possible. It is to use their judgment deliberately, improve the system, and earn greater agent authority one decision at a time.