The Ralph Wiggum Loop

The Ralph Wiggum loop is an orchestration pattern for autonomous AI coding. An orchestrator agent drives subagents through a dependency-ordered task list until an entire project is implemented.

How it works

1. The orchestrator reads PROGRESS.md and features.json to find the next incomplete task
2. It agrees a sprint contract with the subagent before handoff (scope, files, exit criteria)
3. The subagent implements the task, runs preflight checks (build, test, lint), and commits
4. The orchestrator independently re-runs preflight to verify — it does not trust the subagent’s self-report
5. If an [evaluator] agent is configured, it scores each exit criterion and updates features.json
6. The orchestrator marks the task complete in PROGRESS.md, records the codebase state diff and any learnings
7. Repeat until all tasks show [x]

Why it works

• Bounded context — Each subagent only sees one task file, keeping the prompt focused and reducing hallucination
• Dependency ordering — Tasks are topologically sorted, so each subagent builds on verified prior work
• Preflight gates — Every task must pass build/test/lint before being marked complete
• Independent verification — The orchestrator re-runs preflight itself rather than trusting the subagent’s checkbox
• Feature registry — features.json tracks per-criterion pass/fail state as objective ground truth
• Codebase state handoff — Each subagent records what it changed, giving the next one accurate context
• Learnings accumulate — The progress tracker captures insights from each task, providing growing context

Origin

The pattern was proven on the Yakko project, a Rust Microsoft Teams TUI client where 13 tasks across 7 phases were executed sequentially by subagents in a single automated session.

Wiggum makes this pattern reproducible for any project by generating the required artifacts from a structured plan definition.