My LLM kept implementing every method it found, so I added research and specification gates[D]

While building this workflow a thing that surprised me was that, initially I thought the pipeline was complete:
From Goal to
→ Decompose
→ Research
→ Specification
→ Implementation
It successfully broke the problem into relevant blocks, it found research for each block, and it produced detailed implementation specifications.
But it noticed it was not right yet
The problem is that the LLM often tried to implement every relevant method it found.

For example If five papers described five different approaches, the generated implementation would sometimes combine all of them even when the original engineering goal only required one.
Some other times it accepted redundant inputs or introduced unnecessary abstractions because it couldn’t distinguish between:
* useful context
* interesting alternatives
* and the actual design decisions.
And this made me to realise that research should guild the implementation but it should not become the implementation it’s self.
Someone needs to decide which approach they are taking, Which methods needs to be excluded, what assumptions are we making and what exactly needs to be implemented.
That’s why I added a mandatory editing stage to the workflow.
So Instead of immediately generating code after research, the workflow stops.
This means the extracted research can be reviewable.
Which makes the implementation decisions refinable
And by then the final specification can be produced.
The goal isn’t to slow the process down. But It’s to make sure the generated implementation actually reflects the original engineering plan rather than every possibility the model discovered.
Now I’m starting to think that gating AI outputs isn’t just a maybe but it may be one of the most important parts of building reliable engineering workflows.

I’m building this as part of a broader MCP system for decomposing, researching, specifying and implementing deep-learning systems.
If anyone is interested in discussing the system, testing it, or contributing to its development, the GitHub repository is linked in the discussion.

I am also interested to hear how others handle the transition from retrieved research to concrete implementation decisions.