The Autonomous Coding Agent: Running AI Coding Agents

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AI copilots are great at speeding up small tasks, but they break down when the work gets real: multi-step refactors, migrations, test fixes, flaky builds, and “it compiles on my machine” chaos. You end up in a familiar pattern: prompt, patch, rerun, repeat, then babysitting an assistant that can’t reliably cross the finish line.

But the “Ralph Wiggum loop” is a pragmatic alternative: wrap an AI coding agent in a tight feedback loop so it can iterate, validate, and retry on its own until it meets a clear definition of done, or it’s truly blocked. The concept began as a simple looping methodology and has since become an official Claude Code plugin and a broader community pattern for long-running, autonomous coding.

In this session, Byron will break down how the Ralph Wiggum loop works, why it’s effective, and the engineering guardrails that make it safe enough to use on real codebases. You’ll see how to structure tasks so agents can make progress without constant human intervention, and how to design “stop conditions” that prevent infinite loops and wasted spend.

Attendees will learn how to:
- Turn messy work into small, verifiable “agent-sized” tasks
- Define success criteria that the agent can prove (tests, checks, diffs, lint, build)
- Add retry and validation loops without creating runaway automation
- Decide what should be autonomous vs. human-in-the-loop
- Apply this pattern to refactors, migrations, test stabilization, and cleanup work