Building a Context Engine Product for Agents
For Developer tooling founders · Based on Walsenuk Stop Babysitting Agents Framework
// TL;DR
If you're building a developer tooling or AI infrastructure product, the Stop Babysitting Agents framework maps the category you're competing in: the Context Engine. It defines the moat — exhaustive multi-surface retrieval, a procedural social graph, conflict resolution via authority-weighting, permission-scoped responses, and token-optimised research packets. Understanding why naive RAG and raw MCP fail (Satisfaction of Search, access without understanding) tells you where to differentiate. Use this framework to position your product beyond 'another MCP connector' and toward the runtime layer that makes autonomous agents actually merge-ready.
What category are you actually building in?
The Context Engine — the machine layer that replaces the human engineer as the supplier of org-specific context to agents. If your product only offers access to data (an MCP connector, a docs indexer, a vector store), you're building a commodity pipe. The defensible category is understanding: reasoning across every system of record, resolving conflicts, scoping by identity, and returning exactly what an agent needs to act.
Framing your product this way immediately separates you from the crowded 'connect more tools' segment. Access is table stakes; understanding is the moat.
Why do naive RAG and MCP-first competitors leave room for you?
Because they trigger Satisfaction of Search. A vector store or raw MCP call returns the first plausible chunk, the agent stops looking, and it ships duplicate code that gets rejected at review. Founders who understand this failure mode can build the opposite: exhaustive retrieval that fans out across all surfaces in parallel and runs until no new relevant signals remain.
Your differentiation lives in the parts competitors skip — conflict resolution when code in main disagrees with a CTO Slack thread, permission scoping that carries OAuth context through every call, and a social graph generated procedurally from PR history that scopes retrieval to the right engineer.
How do you make your product economically compelling?
Lead with token optimisation. The market misconception is that bigger context windows solve everything — but agents can't reason over a million tokens, and stuffed context inflates cost. Your engine's ability to compress everything it found into a small, high-signal research packet is the feature that makes background agents economically viable for your customers. Sell the cheaper, better runs, not the raw retrieval volume.
Avoid the temptation to cache answers for a latency win. Cached context decays into confident lies within a day in active codebases — building on caching undermines the trust your product depends on. Compete on retrieval architecture speed instead.
How do you expand beyond agents to grow account value?
Position the same engine as multi-surface. It can serve background agents, auto-answer Ask Engineering Slack channels with confidence scoring, enrich tickets, and power incident triage. Founders who ship this compounding leverage give customers many reasons to standardise on one engine, increasing stickiness and expansion revenue.
Also make governance a first-class selling point. Enterprises won't adopt an engine ingesting Slack and Teams unless private conversations are permission-scoped from day one. Building this in — rather than retrofitting — is both a moat and a procurement unlock.
Next step: Map your current product against the Context Engine definition, identify which pillar (exhaustive retrieval, social graph, conflict resolution, permission scoping, or token optimisation) is your wedge, and build a demo showing an agent go from 'rejected at review' to 'nitpick and merge.'
// FREQUENTLY ASKED QUESTIONS
How do I position my product against MCP connector companies?
Position on understanding, not access. MCP connectors are pipes — they let agents reach data but the agent still doesn't know what it doesn't know. A Context Engine reasons across surfaces, resolves conflicts, scopes by identity, and returns token-optimised packets. Frame connectors as table stakes and your reasoning layer as the moat that makes agents merge-ready.
What feature is the strongest wedge for a new Context Engine product?
Exhaustive retrieval that defeats Satisfaction of Search is often the clearest demo, because customers can immediately see agents stop reinventing existing code. Conflict resolution and the procedural social graph are strong differentiators, and token optimisation is the economic hook. Pick the pillar where incumbents are weakest and lead with a before/after PR review demo.
Why shouldn't my product cache context answers for speed?
Because caching a correct answer is equivalent to writing docs — it begins decaying immediately. In active codebases a cached answer is likely wrong within 24 hours, turning your product's correct answer into a confident lie. That destroys the trust your product sells. Compete on retrieval architecture latency instead of answer caching.