How to Build a Context Engine Instead of Naive RAG

Why does naive RAG fail for coding agents?

Naive RAG — dropping a data store in front of an agent and letting it crawl — triggers satisfaction of search. The agent finds the first plausible result and stops, missing the correct architectural pattern or an existing shared service. It also can't reason across distributed or conflicting sources: when code in main contradicts a Slack thread, naive RAG lets the agent pick arbitrarily. The result is code that compiles but breaks the system. A larger context window doesn't rescue this — even a 1-million-token window can't reason over unstructured dumps without entities, relationships, and targeted retrieval.

How should I architect exhaustive targeted retrieval?

Build retrieval in four moves. First, construct a structured query from the agent's task rather than a raw semantic search. Second, traverse all relevant surfaces exhaustively — code, docs, PRs, tickets, Slack/Teams, SaaS data — instead of stopping at the first hit. Third, apply a social graph to scope results: use the requester's identity as a pivot point to select the right codebases and collaborators, so vague prompts produce precise retrieval. Fourth, surface conflicts rather than silently resolving them downstream.

Build the social graph from commit and PR history — who authors what, who reviews whom, who owns which services. Node size can map to commit volume; edges represent review and collaboration. Open-source tooling can generate this procedurally from a git repo.

How do I resolve conflicts between sources programmatically?

When sources contradict, apply authority and recency signals. The social graph informs truthiness: a CTO statement in a recent Slack thread outweighs an incorrect implementation sitting in main. Determine ground truth, then pass both the resolution and its sources to the agent so it understands why. This transparency prevents the unpredictable, sometimes catastrophic outputs that come from hiding conflicts.

Then compress. The engine's job is not to dump everything into the window — it's to reason across surfaces and produce a token-optimized research packet: the patterns in use, relevant existing services, ownership, and constraints. A smaller precise packet outperforms a large noisy one on quality and cost.

Why shouldn't I cache answers for latency?

Because caching a correct answer is equivalent to writing docs — it begins going stale the instant it's written. The same question asked 24 hours later may have a different correct answer because the system changed. Serve fresh context every time and accept the latency cost. Stale context is worse than slow context.

How do I maximize leverage from one engine?

Expose the engine everywhere it adds value, not just the agent harness. Surface it via MCP for coding agents (Plan → Execute → Review calls), auto-detect questions in ask-engineering Slack/Teams channels and respond with a confidence score, enrich and triage tickets, and support incident management. One engine serving all surfaces multiplies your platform's return on the build.

Next step: Prototype exhaustive retrieval against one repo plus its Slack channel, and instrument conflict detection before you scale ingestion org-wide.