# Agent loops: plan, tool, verify, repeat

Whether an AI agent works is decided by the loop, not the model. What I learned about loop design shipping ten apps solo and writing DevDNA.

By: Necati Doğrul · 2026-09-13 · https://necatidogrul.dev/en/notes/agent-loops · ai, agents

"Agent" sounds like a lot of things but is one thing: a loop. Observe, plan, call a tool, verify the result, repeat until a stop condition. A better model does every step a little better; a bad loop makes the best model produce bad results. I call this loop engineering: designing the loop, not the prompt.

## The four parts

1. **Observation.** What can the agent look at? A file system, an API, a browser screenshot. It cannot manage what it cannot see. The biggest win while shipping apps was making the App Store Connect API the agent's eyes: version state, the review cart, price tables all became queryable.
2. **Plan.** Short and reviewable. Long plans go stale with the first tool output.
3. **Tool.** A good tool is narrow, deterministic, and fails honestly. "Submit" on its own is dangerous; it needs a gate in front of it. Ours is `presubmit_gate.py`: no IAP, a US-only price, an unpublished privacy label, all caught, and the agent cannot submit past them.
4. **Verification.** The agent saying "done" is not enough. A screenshot, an HTTP 200, a counter going up. The second-source rule: before reporting a finding, see it another way. Most false alarms died there.

## Memory

This is what I spent the most time on in DevDNA: an agent forgets when the conversation ends. For a team, decisions, standards and habits must live in written memory files, read at the start of every loop. Same in my own workflow: a project note per app, a reference note per trap. The agent's memory is files, not the conversation.

## The stop condition

The most overlooked part. What does "complete" mean? Tests pass, the page returns 200, the user approved? A loop without a written stop condition either quits early or never stops. For hard-to-reverse work (deletion, publishing, money) the stop condition should be a human.

## In short

The model is rented; the loop is yours. Widen the observation surface, narrow the tools and guard them with gates, verify every claim from a second source, write memory to files, write the stop condition first. I shipped ten apps with this loop; the model changed three times, the loop did not.

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Necati Doğrul · https://necatidogrul.dev