OKF + RAG together: give the agent a map, then give it a search

The comparison piece establishes the layers: OKF, RAG, and vector databases each operate at a different layer. The next practical question is how they fit together.

A two-layer architecture: source + retrieval

In practice, the two layers have different jobs. OKF is the source layer: keep curated, structured, trusted knowledge — metric definitions, schemas, runbooks, architecture decisions — in a readable, linkable, versionable format. Vector/RAG is the retrieval layer, added when you need dynamic search over a large long tail.

The agent’s flow: look up first, retrieve second

The agent starts with a structured lookup: enter from the OKF index and traverse by type and links to the nodes it needs. This part is about precision and trust. If the question falls into a large unstructured long tail, structured lookup may not be enough, so the agent adds semantic retrieval. The order matters: establish the trusted part first, then use search to fill the fuzzy gaps.

Why using OKF as the source makes RAG better too

Putting OKF at the source layer does more than add another layer of management; it also improves the retrieval on top. Structure is preserved, so chunks do not shred tables and definitions. Relationships are explicit links, so multi-hop questions have a graph to walk. Each concept has a stable path identity, which makes citations more stable and traceable. Because the source is versionable, the index can be rebuilt from a clean source at any time, and sync problems are easier to investigate.

The OKF v0.1 spec names a search index as one of its consumers, so you can build a vector index straight from the bundle. The combination is a clean source with search available when you need it.

When to add the retrieval layer

Do not reach for the heaviest option first. Follow the decision rule from the comparison piece: start with OKF plus file navigation; add full-text search when navigation starts to miss; add a vector index when queries are frequently paraphrased and fuzzy; and consider a formal knowledge graph only when multi-hop relationships become the dominant problem. Let the observed retrieval failure tell you when to add the next layer.

A useful mental model for the whole architecture is simple: OKF gives the agent a map, RAG gives it a search. The map lets it move precisely where precision and trust matter; the search gives it coverage in the fuzzy long tail. As the knowledge system grows, the two usually work as layers rather than alternatives.