How Do Platform Engineers Build a Context Engine for AI Agents?
For Platform engineers and DevTools teams building internal AI infrastructure · Based on Walsenuk Stop Babysitting Agents Framework
// TL;DR
If you're a platform engineer tasked with making AI agents actually work for your org, you need to build a Context Engine — the infrastructure layer between your agents and your systems of record. This means implementing exhaustive multi-surface retrieval (not naive RAG), a social graph generated from GitHub collaboration data, conflict resolution with authority-weighting, permission-scoped responses via OAuth, and token-optimised output compression. The Walsenuk Framework gives you the architectural blueprint: fan-out retrieval, the three-phase execution loop (Plan → Execute → Review), and a single engine that serves both agents and human surfaces.
What Architecture Does a Context Engine Actually Require?
A Context Engine is not a vector store with a retrieval API. It is a reasoning layer that sits between agents and your systems of record, performing four operations:
1. Structured query construction — transform the agent's intent into a multi-surface research query, scoped by the social graph
2. Exhaustive parallel retrieval — fan out across GitHub, Slack, Jira, docs, and every other system of record simultaneously; run until no new relevant signals remain
3. Conflict resolution — detect contradictions between sources, apply authority-weighting (recency, role, canonicity), surface the resolution with citations
4. Token-optimised compression — reason across all results, strip redundancy, return only the minimum high-signal content the agent needs
This is fundamentally different from naive RAG, which retrieves top-k chunks from a single vector store and triggers Satisfaction of Search — the agent stops at the first plausible result.
How Do I Build Exhaustive Retrieval Without Killing Latency?
The temptation is to cache answers for speed. Don't. Cached context answers decay almost immediately in active codebases — a cached answer to "what is our Zendesk pattern?" becomes a confident lie within 24 hours.
Instead, optimise latency through architecture:
- Incremental indexing — update indices as commits, messages, and tickets arrive, not on a nightly batch
- Parallel fan-out — query all systems of record simultaneously, not sequentially
- Social-graph scoping — use the graph to narrow the search space for each query, reducing the volume of data to process
- Pre-computed relationship edges — maintain the social graph continuously so it's ready at query time
The social graph is your most powerful latency tool because it eliminates irrelevant search space before retrieval even begins.
How Do I Implement Conflict Resolution That Actually Works?
Conflicts are everywhere: code in `main` says REST, a CTO Slack thread says gRPC. A design doc says microservices, a recent architecture review says consolidate. Hiding these conflicts produces confidently wrong agent output.
Implement three authority-weighting dimensions:
- Recency — newer signals outweigh older ones (a decision made last week overrides a pattern from 18 months ago)
- Role — CTO directive overrides a peer's off-hand comment; official architecture review overrides a Slack hot take
- Canonicity — formal documentation outweighs informal conversation, but recent informal conversation from an authority overweighs stale formal docs
Critically: log every detected conflict for human review. Conflicts are architectural signals — they indicate decisions that need to be formalised, docs that need updating, or migrations that are incomplete.
How Should I Structure the Agent Execution Loop?
The Context Engine operates at two junctures in a three-phase loop:
1. Plan with Engine — deliver a research packet before execution so the agent produces an org-aware plan
2. Execute — the agent implements against the plan
3. Review with Engine — evaluate the output against real patterns, past decisions, and current truth
This bookending of execution is what transforms agent output from "this would break the entire system" to "nitpick and merge." The engine isn't just a pre-execution context dump — it's also the automated reviewer that catches when execution deviated from organisational truth.
How Do I Handle Permissions Without Retrofitting?
Carry OAuth-model auth context through every retrieval call from day one. Every query to the engine includes the requesting identity. Every result is filtered against that identity's permissions before inclusion in the research packet. Private Slack DMs, restricted channels, HR-sensitive data — none of it surfaces to unauthorised requesters.
Retrofitting permissions after the engine is already ingesting private conversations is a security incident waiting to happen. Build it in from the start.
What's My Next Step?
Start with the social graph — point an automated tool at your GitHub repositories to generate the collaboration graph. This is the foundation for scoped retrieval and the fastest way to demonstrate value. Then build exhaustive retrieval across your two highest-signal systems of record (usually GitHub and Slack). Add conflict resolution and permission scoping. Ship it serving the three-phase execution loop, then extend to Slack auto-answers, ticket enrichment, and incident triage.
// FREQUENTLY ASKED QUESTIONS
Can I use an existing vector database as the foundation for a Context Engine?
A vector database can be one component, but it is not sufficient on its own. Naive RAG over a single vector store triggers Satisfaction of Search. A Context Engine requires multi-surface retrieval across heterogeneous sources (code, chat, tickets, docs), conflict resolution logic, social-graph-scoped query construction, permission filtering, and token-optimised compression. The vector store handles similarity search; the engine handles reasoning.
How do I index Slack messages without violating privacy?
Implement permission-scoped indexing from day one using an OAuth model. Index messages with their channel permissions and membership metadata attached. At query time, filter results against the requesting identity's access rights before including anything in the research packet. Private DMs and restricted channels never surface to unauthorised requesters. This must be architectural, not a post-processing filter.
Should I build separate Context Engines for different agent tasks?
No. Build a single Context Engine that serves all surfaces — coding agents, Slack auto-answers, ticket enrichment, incident triage, and human engineers asking ad-hoc questions. The social graph and exhaustive retrieval logic is the same; only the query construction and output formatting differ by surface. A single well-built engine delivers compounding leverage across all use cases.