Essential AI Coding Feedback Loops For TypeScript Projects

When AI coding agents operate independently, they need mechanisms to verify their own work. Matt Pocock outlines three essential feedback loops for TypeScript projects.

The Core Problem

AI agents working AFK (away from keyboard)—like those using the Ralph Wiggum technique—can't test changes in a browser. They need automated feedback to catch errors before they compound.

Three Feedback Loops

1. TypeScript Over JavaScript

TypeScript provides free feedback. Type errors surface problems the AI would never find without manual browser testing. The type system acts as a constant sanity check on the agent's work.

2. Vitest for Logic Errors

Types catch structural mistakes but miss logical ones. A test framework like Vitest fills this gap. Basic unit tests covering core functionality keep the AI on track when reasoning fails.

3. Husky Pre-commit Hooks

Husky enforces these feedback loops before every commit. The agent cannot push broken code—it must fix issues first.

# Install and initialize Husky
npx husky init

# Create pre-commit hook
echo "pnpm typecheck && pnpm test" > .husky/pre-commit

Feedback Loop Diagram

flowchart LR
    Code[AI Writes Code] --> TS{TypeScript Check}
    TS -->|Errors| Fix1[Fix Types]
    Fix1 --> Code
    TS -->|Pass| Test{Vitest Tests}
    Test -->|Fail| Fix2[Fix Logic]
    Fix2 --> Code
    Test -->|Pass| Husky{Pre-commit Hook}
    Husky -->|Blocked| Code
    Husky -->|Pass| Commit[Commit]

Connections

• ralph-guide - The Ralph Wiggum technique relies on these feedback loops to run AI agents autonomously overnight
• vue3-testing-pyramid-vitest-browser-mode - Both emphasize Vitest as critical infrastructure for AI-assisted development