In a world where AI orchestration frameworks are becoming increasingly bloated with "agentic" complexity, Matt Pocock recently highlighted a refreshingly simple approach: The Ralph Wiggum Technique (named after a minimalist strategy popularized by Geoffrey Huntley).

The core philosophy? Unbundling orchestration. Instead of building a complex system of semi-autonomous agents that talk to each other in a black box, you use the simplest loop possible to drive an AI tool toward a goal.

The Architecture of "Just a Loop"

The technique relies of three pillars: atomic tasks (PRDs), a simple loop, and robust feedback. By breaking a large project into a directory of small, well-defined Markdown files (Product Requirement Documents), you can feed them sequentially into an AI coding tool like Claude Code or Aider.

# The "Purest" Form
while :; do cat PROMPT.md | npx --yes @sourcegraph/amp ; done

Or Matt Pocock's pragmatic iteration version:

for file in backlog/*.md; do
  claude-code "$file"
  git add .
  git commit -m "ralph: completed $file"
done

Why It Works: The Supervisor is the Compiler

The brilliance of this technique is most apparent when working with TypeScript. You don't need a "Reviewer Agent" to check the AI's work. The TypeScript compiler and your unit tests act as the ultimate supervisors. If the agent makes a mistake, the loop continues (or the next task fails), providing the agent with the exact error messages it needs to fix itself.

Ship While You Sleep

By unbundling orchestration, you regain predictability. You can define a backlog of 20 tasks, start the loop, and go to bed. The agent will churn through each task, committing as it go. It's atomic, it's traceable, and it's incredibly effective.

Sometimes, the best AI strategy isn't more complexity—it's just a better loop.