How to Build a Context Engine for Coding Agents

Why do agents fail even when they have access to all your tools?

Because access is not understanding. Wiring MCP pipes to GitHub, Slack, and Jira lets an agent reach your data, but the agent still doesn't know what it doesn't know — exactly like a day-one engineer unaware that a shared service already exists. As an infrastructure engineer, your job isn't to add more pipes; it's to build the layer that reasons across those pipes and returns understanding. That layer is the Context Engine.

The default failure mode you're fighting is Satisfaction of Search: the agent latches onto the first plausible chunk it retrieves, stops looking, and ships code that duplicates existing utilities or ignores the canonical pattern. Naive RAG over a vector store makes this worse, not better.

How do you architect exhaustive, multi-surface retrieval?

Don't stand up a single vector store and call it done. Build retrieval that does four things: constructs a structured research query from the agent's intent, fans out across all systems of record in parallel, runs exhaustively until no new relevant signals remain, and reasons across results before returning anything.

Then layer in conflict resolution. When the code in main says REST and a CTO Slack thread says the team moved to gRPC, your engine applies authority-weighting — recency, role, and canonicity — surfaces the conflict with citations, and delivers the settled truth. Never let the agent silently pick. Log every conflict for human review; they're architectural signals about where your sources have drifted.

How do you make background agents economically viable?

Through token optimisation, not bigger context windows. Large windows don't help agents reason — they cause them to fail. Your engine should reason across everything it retrieved, strip redundancy, resolve conflicts, and return a small, high-signal research packet. Smaller curated packets produce better plans and cheaper runs, which is the entire economic argument for running agents in the background.

Build the social graph procedurally from your repo — PR reviews, co-authorship, service ownership as edges. This is your pivot point for personalised retrieval, scoping each query to the right people, codebases, and history. Never maintain it by hand; it decays like any static doc.

How do you enforce governance from day one?

Carry auth context — an OAuth model — through every retrieval call. Private Slack DMs and restricted channels must never surface to a requester lacking permission, even when the query would benefit. For orgs of 20+ engineers this is mandatory and must be designed in, never retrofitted after you're already ingesting Slack and Teams.

Finally, resist caching context answers. A cached correct answer to 'what is our Zendesk pattern?' becomes a confident lie 24 hours later. Optimise for latency through retrieval architecture instead.

Structure agent runs as Plan with Engine → Execute → Review with Engine, and extend the same engine to non-agent surfaces — Ask Engineering channels, ticket enrichment, incident triage — for compounding leverage.

Next step: Diagnose your team's position on the Context Ladder, audit every system of record, and generate your social graph procedurally from your GitHub PR history before you write a single line of retrieval code.