I’ve been exploring this too, since I rely on LLMs a lot to build software. I’ve noticed that our dev loop-writing, testing-is often mostly human-guided, but language models frequently outperform us in reasoning. If we plug in more automation; MCP tools controlling browsers, documentation readers, requirement analysers, we can make the cycle much more automated, with less human involvement.
This article suggests scaling up RL by exposing models to thousands of environments
I think we can already achieve something similar by chaining multiple agents:
1. A “requirement” agent that uses browser tools to craft detailed specs from docs.
2. A coding agent that sets up environments (Docker, build tools) via browser or CLI.
3. A testing agent that validates code against specs, again through tooling.
4. A feedback loop where the tester guides the coder based on results.
Put together, this system becomes a fully autonomous development pipeline-especially for small projects. In practice, I’ve left my machine running overnight, and these agents propose new features, implement them, run tests, and push to repo once they pass. It works surprisingly well.
The main barrier is cost—spinning up many powerful models is expensive. But on a modest scale, this method is remarkably effective.
This article suggests scaling up RL by exposing models to thousands of environments
I think we can already achieve something similar by chaining multiple agents:
1. A “requirement” agent that uses browser tools to craft detailed specs from docs.
2. A coding agent that sets up environments (Docker, build tools) via browser or CLI.
3. A testing agent that validates code against specs, again through tooling.
4. A feedback loop where the tester guides the coder based on results.
Put together, this system becomes a fully autonomous development pipeline-especially for small projects. In practice, I’ve left my machine running overnight, and these agents propose new features, implement them, run tests, and push to repo once they pass. It works surprisingly well.
The main barrier is cost—spinning up many powerful models is expensive. But on a modest scale, this method is remarkably effective.