Parallel AI Coding

Parallel Coding: Using AI Agents Like a Development Team

For a long time, I’ve used AI-powered coding tools inside modern IDEs, and they’ve been incredibly useful. But more recently, I’ve started to realise the real power of parallel and recursive AI agents.

Here’s how it works.

You start a lead agent and ask it to create a working document for the feature you want to build. You then give it permission to spin up additional agents using Codex to handle sub-tasks in parallel. The lead agent can break the feature into parts, delegate the work, and bring the results back together.

What makes this approach so powerful is that you’re no longer relying on a single AI assistant. Instead, you’re creating something that behaves more like a small development team, with a project lead coordinating multiple contributors at once.

And when it works well, the speed difference is massive.

Features that might once have taken days or even weeks can start coming together in hours. You’re not just getting closer to real-world software delivery workflows — in some ways, you can move even faster, especially when you’re small, focused, and pushing toward an MVP.

At larger scale, traditional delivery controls still matter. You need gating, reviews, extra checks, and stronger validation. But that’s a good problem to have. In the early stages, when speed matters most, parallel AI coding gives you something incredibly valuable: more time back to spend on what actually matters.

That might mean refining the product, talking to users, validating ideas, or simply building more before momentum fades.

The real shift is this: AI stops being just a coding assistant and starts acting more like a coordinated development team.