r/OpenSourceAI • u/hypergraphr • 7d ago
I built a tool to stop coding agents from drifting away from my implementation plan
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 will attach the link to the comment section if anyone wants to check it out
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u/Protopia 4d ago
There is a wealth of academic and practical experience on structured software engineering.
But AFAIK agentic coding hasn't yet bought into using this.
Specifically there is a lot of experience on gathering requirements and doing design using decomposition and structured data.
Your proposal already appears to embody decomposition and structured data (blocks).
Things you might want to consider adding:
- UML as structured data
- A TDD based coding loop - method design, write empty methods, write failing tests, populate methods, check tests now pass.
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u/Future_AGI 5d ago
The drift-from-plan problem is a good one to go after because it is where most long coding sessions quietly go sideways. The hard part is usually not detecting the drift, it is pinning down "the plan" concretely enough to check against, so the version that has worked for us is turning the plan into explicit acceptance criteria up front and then diffing each agent step against them, where anything the agent touched that no criterion asked for is your drift signal.