Seven AI Coding Agent Loops and When to Use Them

A loop lets an AI coding agent work repeatedly toward a specified goal. I find loops useful because the agent can continue iterating without a person manually prompting every step. That autonomy does not mean the result is automatically correct or that the work needs no oversight.

I describe two parts of a loop: a trigger and a goal. The trigger may be a person starting it, a schedule, or an event such as opening a pull request. A recurring trigger is useful for maintenance; a manual trigger is useful when the work should begin only on request.

A goal can be checked against a concrete condition or judged by a language model. For example, coverage can be measured numerically, while “refactor until satisfied” leaves the model to assess a subjective outcome. Even a measurable metric such as coverage does not by itself establish that tests are meaningful.

I have collected examples in a free Loop Library so people can copy prompts or adapt them. The examples below show different triggers and stopping conditions; they are starting points that need to be tailored to a project and its constraints.

The first is a page-load optimization loop. After each significant change, the agent measures every target page under the same repeatable conditions and continues until each meets a sub-50-millisecond threshold. Defining the measurement and test conditions is essential: a result measured in one environment does not establish the same experience for all users.

This loop can be started manually, scheduled or triggered by a pull request. I demonstrate copying the prompt into Codex and setting it as a goal. I say the agent may work for minutes or hours, so users with limited token budgets need to watch usage and set appropriate bounds.

I show a run that worked for nearly 50 minutes on a production performance goal. The visible agent output identifies a network latency floor and database round trips, and says a true cold load cannot reach 50 milliseconds under the conditions described. The clip illustrates an investigation, not independent proof that every production page achieved the target.

The overnight docs sweep asks an agent each night to review the codebase, update documentation to reflect recent changes and open a pull request. The objective is to keep implementation and documentation aligned while producing changes a person can review.

This is partly an LLM-as-judge task: deciding whether the documentation is complete involves judgment, even if specific coverage checks can be automated. I demonstrate creating a recurring Codex automation with the copied prompt. A scheduled review is a proposal for a workflow, not proof that every document will always be correct.

Peter Steinberger’s architecture satisfaction loop asks the agent to refactor until it is satisfied with the architecture. The prompt adds structure: after each significant step, live-test the system, run an automated review, commit a checkpoint and track progress in a Markdown file.

Because satisfaction is subjective, I suggest providing additional guidance such as favoring simplicity or reducing duplication. The agent’s judgment can still differ from the team’s architectural goals, so the tests, review and recorded checkpoints matter.

The logging coverage loop reviews the system and adds missing logs until important paths produce useful, tested events. The word “important” makes the stopping condition partly subjective: the agent must decide which paths matter. This loop also sets up a later workflow that uses the resulting logs.

The production error sweep runs on a schedule and reviews production logs. When it finds an actionable issue, the prompt asks the agent to trace the cause, fix it, verify the fix and open a pull request, then report the findings and link in Slack. If there are no actionable errors, it reports that result instead.

The SEO/GEO visibility loop audits crawlability, indexation, page intent, titles, internal links, structured data, citations and answer-first content. It ranks gaps, fixes the highest-impact issue, reruns the same crawl and benchmark, and repeats until its specified critical gaps are addressed.

The full product evaluation loop starts by creating realistic scenarios across major capabilities. Before testing, it defines success criteria and a consistent method, such as pass/fail checks or a scoring rubric. It runs scenarios under the same conditions and records evidence for each outcome.

For outcomes that fail, the agent investigates the underlying cause, makes changes, reruns affected scenarios and then reruns the complete set. I describe this as more than a conventional deterministic test suite because some judgments about quality can be made by a model.

I say this loop can take twelve hours or more, and offer an example of a question-answering product that must provide accurate, sourced responses. The agent can create a wide range of test questions, judge the results and iterate, but its judgment and chosen scenarios still need scrutiny.

There are two major caveats. First, designing a useful stopping condition is difficult, and loops are not appropriate for every problem. A concrete, verifiable target is easier to assess than a subjective one where the model must decide whether its own work is good enough.

Feature development is especially hard to leave open-ended. Asking an agent to build a complete permissions system without specifying the desired behavior gives it too much freedom to choose what matters and when to stop. I prefer not to use such a loop for an unspecified feature from day zero.

I once asked an agent to clone Excel to feature parity. It used computer control to inspect Excel, but the task ran for days before I stopped it. I do not recommend that approach: an expansive goal can keep producing work without a practical endpoint.

Second, loops can be expensive. Agents consume tokens as they work, and a run can last minutes, hours or days. Consider the budget and the value of the task before allowing an autonomous loop to keep going.