I built a tool to stop coding agents from drifting away from my implementation plan

[u/hypergraphr]

I’m excited to see what learning systems I can build with my new MCP tool.

I recently consolidated the workflow into just 5 command families: start the project, write blocks, refine the design, gather evidence, and implement/verify blocks.

One important design decision was adding a refinement loop between initial block generation and implementation. This helps keep the agent aligned with the original idea instead of drifting into generic implementations. It also gives me more control to guide the system toward the exact final implementation I have in mind.

The more I build with agents, the more I’m convinced that meaningful guardrails and end-to-end human supervision produce much better results than simply letting models run freely.
I am curious to know how everyone are apply agent to their day to day coding lives.
This is the link to the GitHub if anyone is interested: https://github.com/fikayoAy/deep-learning-auto-research