Run Autonomous Coding Sessions That Do Not Break

Run Autonomous Coding Sessions That Do not Break

You have used an AI coding agent. You know the feeling: it works great for the first ten minutes. Then it hits an error, gets stuck in a loop, generates nonsense, or just stops responding. And you are back to doing it yourself.

The promise of AI coding agents is autonomous work. The reality is constant hand-holding. You babysit the agent more than you would a junior developer. Every few minutes, you check in. Every error, you debug. Every failure, you restart.

This is not the agent is problem. It is the loop is problem.

A good agent can write code. A good loop keeps the agent productive without you standing over its shoulder. The difference between an agent that crashes and burns and one that actually delivers is what happens when things go wrong — and they always go wrong.

Coding Agent Loops is a skill that gives your OpenClaw self-healing, persistent coding sessions. It wraps your AI agent in a tmux-based loop that handles errors, retries failed commands, manages context, and keeps working until the task is done or hits a hard limit you define.

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Key Takeaways

• AI agents fail; loops recover. The difference between a broken session and a working one is error handling.
• tmux is the backbone. Persistent sessions mean you can detach and come back later.
• Self-healing means autonomy. Set it, forget it, come back to done work.
• Coding Agent Loops gives you all of this out of the box — no configuration required.
• Start small. Test on low-stakes tasks before trusting it with production code.

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Why Coding Agents Keep Breaking

Let is list all the ways an AI coding agent can fail in a single session:

It hits an error and stops. The agent runs a command, the command fails, and the agent does not know how to recover. It either loops on the same failed command or just gives up.

It loses context. Long-running tasks consume context tokens. The agent starts forgetting earlier parts of the conversation. Code that was written in step one gets overwritten in step ten.

It generates bad code. The agent is confident but wrong. It writes tests that pass but do not test the right thing. It refactors code that did not need refactoring. Without oversight, it digs a deeper hole.

It hangs forever. Some commands do not have timeouts. The agent waits. And waits. And you are stuck waiting with it.

It gets stuck in a loop. Error -> retry -> same error -> retry -> same error. Forever. Without intervention, the agent will happily waste compute on the same failed approach indefinitely.

These are not edge cases. These are the common case. Every developer who has used AI coding agents has experienced all of these. The agent is not the bottleneck — the loop is.

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What a Real Coding Loop Does

A proper coding loop is not just "run this command repeatedly." It is a system that:

1. Executes commands in a controlled environment. tmux sessions isolate the agent is work. If something goes wrong, the session state is preserved. You can attach, see what happened, and manually recover.

2. Handles errors gracefully. When a command fails, the loop analyzes the error. Is it a transient failure (network blip, flaky test)? Retry with exponential backoff. Is it a real problem? Log the error, try an alternative approach, or escalate to a human.

3. Manages context proactively. The loop monitors token usage. When context gets tight, it summarizes earlier work, archives unnecessary history, or asks the agent to consolidate before continuing.

4. Enforces boundaries. You set max iterations, timeouts, and budget limits. The loop stops when it hits them — no runaway loops that cost you $100 in API calls.

5. Logs everything. Every command, every response, every error. When you come back, you see exactly what happened and can pick up where you left off.

This is what Coding Agent Loops gives you. It is a production-ready loop system that turns a flaky agent into something you can actually trust.

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How the Loop Works

Here is the flow from the outside:

Step 1: Define the task. You give the agent a goal. "Refactor the authentication module." "Add tests for the payment flow." "Migrate from Express to Fastify."

Step 2: Configure the loop. You set parameters: max iterations, timeout per command, max context tokens, retry strategy. The loop reads these and enforces them.

Step 3: Run and detach. The loop starts executing. You can detach from the tmux session and go do something else. The loop keeps working.

Step 4: Monitor or intervene. You can attach at any time to see progress. If the agent is going down a bad path, you can kill the session, fix the prompt, and restart.

Step 5: Collect results. When the loop finishes (success, failure, or limit hit), you get a summary. All logs are preserved. You can review what was attempted, what worked, and what needs manual help.

This is the difference between an agent that needs constant attention and one that delivers while you focus elsewhere.

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When to Use Coding Loops

Not every task needs a loop. Here is when it makes sense:

Multi-step refactors. Anything that involves more than two or three sequential changes. A loop can handle a refactor across ten files while maintaining consistency.

Bulk operations. Running migrations, generating boilerplate, applying lint fixes across a codebase. Tasks that are repetitive and well-defined.

Exploratory work with guardrails. When you want the agent to try multiple approaches, but need it to stop if nothing works. The loop provides the safety net.

Long-running tasks. Anything that will take more than a few minutes. Detach, come back later, see progress.

Off-hours work. Set up a loop before you go to bed. Wake up to completed work (or a clear error log explaining why it did not work).

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Common Loop Mistakes

Setting too high limits. If you set max iterations to 100 and timeout to an hour, you might come back to a $500 bill and an agent that has been writing bad code for the last hour. Start conservative.

Not checking early. Detach does not mean ignore. Check in every 15-30 minutes, especially on early runs with a new task. Verify the agent is on the right track.

Ignoring logs. The logs tell you everything. If you are not reading them, you are flying blind. Review them after every session.

Running against main. Never trust an agent loop with your main branch. Always run against a feature branch. Better yet, have the loop create its own branch and commit progress as it goes.

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Next Steps

Here is what to do right now:

1. Install tmux if you do not already have it. Spend ten minutes learning the basics — sessions, detach, attach, panes.

2. Pick a small, real task in your current project. Something with clear success criteria, like "get all tests passing" or "add input validation to the API endpoints."

3. Grab the Coding Agent Loops from Claw Mart and set it up against that task. Follow the included configuration guide to point it at your project and agent of choice.

4. Run it, detach, and check the logs when it is done. Evaluate the output. Tweak the configuration based on what you see.

5. Scale up gradually. Start with small tasks, build confidence in the loop is behavior, then move to larger, multi-step work.

The point is not to replace your judgment. It is to stop replacing your time. Let the loop handle the retry cycle so you can focus on the work that actually needs a human brain behind it.