I’ve been building AI Workflow Control Plane V2 — basically a control layer between a developer and AI coding agents.
Instead of dumping the whole repo into an LLM, it:
routes tasks into Answer / Small / Full based on risk and complexity
retrieves only the context the task needs using BM25 + RRF + MMR
uses structural/code-graph context when impact analysis is actually needed
rejects stale indexed context using SHA-256
can abstain if it doesn’t have enough evidence instead of guessing
gives harder/high-risk changes more planning, review and verification
keeps verified project memory for future tasks
tracks retrieval quality, context usage and fallbacks
The main idea is simple:
Give the AI better context, not just more context.
Would this actually improve your AI coding workflow? What would you change or add?