Flow2Spec Routes Project Knowledge So AI Agents Stop Asking the Same Questions
Context-window bloat and repeated repository searches are a direct cost for teams using AI coding assistants. A deterministic, in-repo routing protocol makes knowledge reviewable in pull requests and prevents agents from acting on partial information, which matters most for hard constraints like idempotency, permissions, and data boundaries where a near-match isn't good enough.
AI coding tools burn context and miss constraints when they load monolithic rule files or re-search a repository from scratch. Flow2Spec replaces that with a four-layer knowledge structure inside the repo: a routing manifest, keyword matchers, topic summaries, and long-form docs. An agent follows a match→expand→verify→act pipeline that checks for information gaps before executing, and writes discovered facts back through structured deltas with optimistic revision locking to prevent stale overwrites in team settings. Task state stays local per developer; confirmed knowledge enters version control alongside code.
Deterministic routing is a deliberate trade-off that accepts lower recall for auditability. For constraints that cannot tolerate a probabilistic match, this is the right call, but teams will still need fallback search for unanticipated queries.
The revision-locking mechanism prevents text-level merge conflicts but deliberately punts semantic conflict resolution to a human. This is honest about the limits of automation: two non-overlapping text additions can still contradict each other as business rules.
Separating personal task state from team knowledge avoids a common failure mode where AI-generated checklists and temporary notes pollute the shared repository, but it also means the team loses visibility into what agents are doing mid-task unless they build separate observability.