Building DevSwarm: Creating the First Production Platform for Parallel AI Coding

From internal tech agency need, to next-gen Augmented Development Environment for parallel AI coding

When our engineers began using AI coding assistants every day, we saw enormous potential. The problem was the surrounding tooling. Each assistant came with its own setup, its own terminal, and its own flow. Context switching multiplied, debugging became harder, and our IDEs felt stuck in the pre-AI era.

We thought there had to be a better way. So we built it.

This is the story of how Twenty Ideas designed, dogfooded, and shipped DevSwarm, and how a small internal alpha helped us deliver a public Beta in five months while preserving production quality.

The challenge: cutting-edge tools, outdated processes

As a product studio working at the frontier of AI, we constantly evaluate new tools. Claude Code, OpenAI Codex, Gemini Code Assist, Amazon Q, Aider, and Goose were all useful in different ways. Yet none of them plugged cleanly into a single, team-ready workflow.

IDEs were built around one snapshot of a codebase at a time. Each AI assistant ran like a separate island. Teams lost time switching contexts, reproducing results became difficult, and it was easy for experiment results to collide in the repo.

We needed an environment that treated AI assistants as first-class team members. The goal was clear: enable parallel AI coding so engineers could run several assistants on the same project and compare results without stepping on each other’s work.

The idea: use AI to build the tool that builds itself

Our approach was straightforward. Let developers connect multiple AI assistants to their workflow, then run those agents in parallel on isolated branches, with humans firmly in the loop. We wanted a system that made comparison trivial and kept code quality top of mind.

Step one was an internal alpha. We used it to coordinate parallel work streams: feature builders, test builders, and bugfix builders running simultaneously. Branch isolation prevented collisions. Inline diffs and small change sets made review fast. Prompt lineage and audit trails preserved reproducibility.

We then used the alpha to build itself. Writing tests, running Playwright-driven checks, and iterating on the UX all happened inside DevSwarm. That feedback loop was the single biggest multiplier for progress.

The result: DevSwarm Beta, built fast and built for quality

By integrating DevSwarm Alpha into our own stack, we compressed weeks of work into days. Engineers could generate multiple candidate solutions from different assistants, compare them side by side, and land the best version with confidence.

Key outcomes:

• We used the internal alpha to accelerate our roadmap and ship the public Beta in five months.
• Developers experienced a step-change in velocity. Tasks that used to line up serially could now be done in parallel. That resulted in bulk speedups: multiple ideas to merged in minutes.
• We preserved quality with small reviewable diffs, tests, and reproducible runs.
  

What we built: parallel AI coding without the chaos

DevSwarm is an Augmented Development Environment that focuses on real engineering needs:

• Parallel Builders and branch isolation. Each Builder runs on its own branch so experiments never collide. You can run a test Builder, a feature Builder, and a bugfix Builder on the same repo at once
• Freedom to choose your own assistant. Use Claude Code, Codex, Gemini Code Assist, Amazon Q, Aider, Goose, or other agents. Bring your own models and mix cloud and local assistants. No lock-in.
• IDE or DevSwarm ADE. DevSwarm provides a code explorer, editor, and diff views so you can review and commit inside the ADE. If you prefer to dive into a branch in your IDE, run it alongside DevSwarm. The choice is yours.‍
• Hotkeys and flow-first navigation. Keep hands on the keyboard with single-keystroke moves between Builders and branches.
• Zero port collisions and smart session handling. We manage ports and sessions so sidecar tools do not fight with each other
• Lineage and audit trails. Every change includes prompt metadata, model versions, and the diff for reproducibility and later review.
• MCP integrations and safe tool hooks. Run Playwright for E2E checks, wire Figma context providers, or connect Context7 for up-to-date code docs.
  

A principle: bring your own agent

From day one we made DevSwarm agent-flexible. Lock-in kills experimentation. Teams should be able to mix proprietary APIs, open-source agents, and local models depending on cost, latency, and security needs. That flexibility is central to how we designed the product.

From in-house tool to community accelerator

What began as an internal utility quickly became something more. After dogfooding DevSwarm, our team realized the platform solved broader problems that other engineering teams face. Rather than keep the advantage to ourselves, we packaged DevSwarm as a Beta and made it available to the community.

Developers can download DevSwarm for macOS and Windows. The Beta is free for a limited time so engineers can try parallel coding, test the UX, and help shape the product.

What we learned

A few practical lessons from building with DevSwarm while building DevSwarm:

• Build your feedback loop into the tool. Using the product to develop the product surfaces real UX and reliability problems far faster than simulated tests.
• Small diffs beat big blobs. Human reviewers can reason about a change set much faster when it is small and focused.
• Reproducibility is non-negotiable. If you cannot reproduce a run, you lose trust in the system. Track models, seeds, and environment metadata.
• Choice scales adoption. Let teams pick their assistants and run local models when they need to. Flexibility encourages experimentation and long term adoption.
  

Final thoughts

We built DevSwarm because we needed it for real production work. The point of HiVE coding is not to replace engineers. It is to amplify what skilled developers already do well: design, review, and ship reliable software fast.

If you are a developer or engineering leader interested in parallel AI coding, try DevSwarm Beta and see how it fits your workflow: https://devswarm.ai