I’m glad I found this post by John. His approach with Claude Code, MCP, and agents is sort of similar (though way more advanced) to how I’ve been using AI in my coding projects (just so you know, I’m not a developer).
Here’s what works best for me: I have a reasoning model generate a task plan based on my requirements. Then, I tweak it (or have the model in Cursor update it) as I go. When bugs pop up, I have the model update the list. This approach keeps the AI from getting lost or confused in a huge context. It also stops it from ignoring bugs or requirements. This has been the most efficient way for me so far.
I keep several claude code windows open, each on its own git-worktree. o3 and sonnet 4 create plans, sonnet 3.7 or sonnet 4 execute the plan, and o3 checks the results against the original ask. Any issues found are fed back into the plan template and the code is regenerated. The factory improves itself.
Source: john-rush.com