AI iteration loop
The ongoing process of correcting, tuning, and expanding an AI agent's behavior based on production feedback.
The iteration loop is the work of making an AI agent better after it ships. It’s distinct from the build loop (writing the initial prompts and skills) and the eval loop (running synthetic test cases against the agent before deployment).
In a production iteration loop, the operator:
- Watches the agent’s outputs in production. Collects approvals, edits, rewrites, escalations.
- Categorizes the misses. Why did the team rewrite this draft? What pattern does the rewrite belong to?
- Picks the highest-impact fixes. Usually a small number of changes — a guardrail addition, a few-shot example, a KB article rewrite — cover most of the rewrite volume.
- Ships the changes. Updates the agent’s prompts, skills, KB, or R.I.D.G.E. card. Logs the change.
- Watches the next week. Did approval rate move? Are the same misses recurring or new ones?
The cadence is typically weekly for production agents, sometimes more frequent in the first month after launch. The iteration loop is where AI agents earn their compounding curve — the gap between an agent that gets better every week and one that drifts unsupervised.
Most ops teams don’t have time to run the iteration loop themselves. That’s why managed AI agent vendors exist: the vendor runs the iteration loop for many customers, learns from repeated patterns, and ships better fixes than any single customer could.
A well-run iteration loop is the difference between an AI agent that adds value for years and one that quietly gets turned off. RidgeHQ’s retainer model is built around running the loop on the customer’s behalf, weekly, forever — or as long as the customer keeps the retainer.