Loop

A loop, in AI customer operations, is the repeating cycle of reviewing an agent's conversations, identifying what went wrong or well, and feeding that back into the agent's instructions so performance improves over time.

It is the mechanism behind self-improvement, not a synonym for it: self-improvement is the outcome, the loop is the process that produces it. A loop typically has three stages: review real conversations against outcomes, extract patterns from the misses, and update the agent's instructions or examples before the next batch of cases. Running the loop consistently is what separates an agent that improves from one that stays static after launch. Where the loop is run properly, every change is reviewed and merged only when the checks pass.

Frequently asked questions

Who runs the loop, a human or the AI agent itself?

Both, in most deployments. The AI agent surfaces conversations worth reviewing, but a human, often on the customer operations or QA team, decides what the feedback should be and approves the update before it goes live.

How is a loop different from retraining a model?

Retraining changes the underlying model on new data. A loop changes what the agent is told to do, through instructions, examples, and guardrails, on top of a model that stays fixed. It is a faster, more auditable way to improve performance.

What happens if the loop does not run?

The agent's performance on new patterns of query stalls at whatever it achieved at launch. Without a loop, an agent has no mechanism to close the gap between what it gets right today and what it could get right tomorrow.