How to Build Reliable Loops for AI Coding Agents

TL;DR

Use an AI loop when a task repeats, has a clear completion condition, can justify repeated token use, and gives the agent every tool required to finish and verify the work. A reliable loop combines a trigger, battle-tested execution skills, a verifiable goal, a defined output, and persistent memory so it can repeat intelligently instead of starting from scratch.

Transcript

Boris Cherny, the creator of Claude code, just said something that should change how you use AI forever. He said, "I don't prompt Claude anymore. My job is to write loops." Then I found a tweet from Peter Steinberg, the creator of Open Claude, saying the exact same thing. You shouldn't be prompting coding agents anymore. You should be designing loo... Read More

Key Insights

• A loop is a prompt-driven process that runs repeatedly until a specific task or goal is complete. It shifts the operator's role from manually issuing many prompts to designing the system that decides what to do next and continues working toward a stopping condition.
• A suitable loop candidate satisfies four conditions: the task repeats, completion can be defined, repeated token usage is affordable, and the agent has all necessary tools. A one-time task is better handled with a normal prompt because recurring orchestration adds unnecessary structure.
• A trigger is the mechanism that starts a loop. The examples include a local recurring command, a cloud-based schedule, and a custom orchestration skill containing the goal, execution process, verification method, and settings required to coordinate the entire workflow.
• Execution skills are saved instruction sets for specialized jobs that an orchestration process calls while running. Battle-tested skills matter because they preserve preferences and procedures, helping the agent perform recurring work consistently instead of improvising its approach during every cycle.
• A goal is useful only when the loop can verify that it has been achieved. For a website launch, verification can include opening the domain, confirming expected content, measuring whether loading takes under two seconds, and requiring approval from an engineering review skill.
• Abstract quality can become verifiable through review skills that return explicit judgments. An email drafting loop, for example, can confirm that every unanswered message has a draft and require approval from email review, writing voice, and fact-checking skills before declaring completion.
• Separate-agent verification can reduce bias when evaluating a loop's output. The suggested approaches include using a different AI through a plugin or asking the primary system to create subagents that independently inspect the work before the orchestration accepts the result.
• Persistent memory prevents a loop from starting without knowledge of earlier attempts. A simple document can record run history, results, problems, and lessons learned, reducing repeated mistakes and wasted tokens while giving future cycles durable context for improving execution.

Questions & Answers

Q: What is loop engineering for AI agents?

Loop engineering is the practice of designing a prompt-driven process that runs repeatedly until a specific task or goal is complete. Instead of manually prompting an AI many times, the operator creates an orchestration that decides what action to take, calls specialized execution skills, checks the result against verification rules, produces an output, and records useful history for later cycles.

Q: When should you use an AI loop instead of a prompt?

Use an AI loop when the task repeats, has a clear definition of done, can justify potentially heavy token use, and gives the agent every tool needed to perform and verify the work. If any condition is missing, the workflow may not be a strong candidate. A one-time task should generally use a normal prompt because it does not require recurring orchestration.

Q: What are the main building blocks of a successful AI loop?

A successful loop needs a trigger, specialized execution skills, a goal paired with verification, a defined output, and persistent memory. The trigger starts the process, the skills perform individual jobs, verification determines whether the goal is complete, the output captures what the process creates, and memory records run history and lessons so future cycles do not repeat earlier mistakes.

Q: How can an AI loop be triggered automatically?

An AI loop can begin through a local recurring command, a cloud-based schedule, or a custom orchestration skill. A local loop can run at a chosen interval but stops when the machine is closed. A cloud schedule runs remotely at selected times. A custom orchestration skill packages the loop's goal, settings, execution process, and verification rules behind a reusable command.

Q: Why should AI loops use battle-tested execution skills?

Battle-tested execution skills give the agent saved, repeatable instructions for specific jobs and preserve important preferences that a generic prompt may miss. For example, a weather workflow without personal workout guidance might recommend canceling a run in the rain. A tested workout-analysis skill could know that rain is acceptable, producing a response aligned with the user's established preferences.

Q: How do you define verification for a technical AI loop?

Technical verification should convert the goal into observable checks that the agent can perform with available tools. For a website launch, the loop could open the specified domain, confirm that expected content appears, measure whether the site loads in under two seconds, and request approval from an engineering review skill. The loop repeats until those checks confirm completion.

Q: How can an AI loop verify subjective or non-technical work?

A non-technical loop can bridge subjective quality to an explicit decision by using specialized review skills. An email workflow might verify that every unanswered message has a draft, then require approval from email review, writing voice, and fact-checking skills. Those reviewers can return an approved or not-approved judgment, or a score from one to ten, giving the loop a stopping rule.

Q: Why does an AI loop need persistent memory?

Persistent memory records what happened during earlier runs, including results, obstacles, and lessons learned. Without that record, each loop begins from scratch and may waste tokens encountering the same problems repeatedly. The memory can be as simple as a document containing run history and notes, while outputs can include documents, code changes, live sites, or messages.

Summary & Key Takeaways

• Loop engineering replaces repeated manual prompting with a system that prompts an AI agent until a defined goal is complete. The suggested screening test asks whether the task repeats, has a clear definition of done, can tolerate potentially heavy token use, and provides all tools needed for implementation and verification.

• A loop begins with a trigger, such as a local recurring command, a cloud schedule, or a custom orchestration skill. The orchestration coordinates specialized execution skills that encode consistent instructions for individual jobs. The recommended approach is to build loops only on skills that have already been tested through repeated use.

• Goals and verification must be designed together because a loop needs an observable stopping condition. Technical results can be checked through tests such as availability, expected content, and loading speed. Abstract work can be reviewed by dedicated skills that return approval decisions or scores, while memory files preserve history and lessons.