How to Generate Mergeable Code with a Context Engine — Peter Werry, Unblocked
The presentation introduces the concept of a Context Engine designed to overcome limitations in current AI agents. Agents often suffer from 'satisfaction of search' and lack deep organizational context (intent, conventions, past decisions), behaving like new employees who reset their knowledge for every task. A Context Engine solves this by ingesting data from diverse sources—including GitHub PRs, Slack discussions, and architecture documents—to provide a comprehensive understanding that allows agents to generate accurate plans, show their work, and prevent compounding errors during complex development tasks.
Key takeaways
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The Context Problem in AI Agents
6:59
Agents struggle because they lack access to 'unknown unknowns'—the critical organizational context like team conventions or architectural rationale. Simply attaching a wiki is insufficient; the agent needs guided, synthesized information.
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The Compounding Effect of Context
16:43
The true value of a context engine is not in solving the first task, but in preventing compounding errors. Without proper context, agents may operate on wrong assumptions, forcing costly loops and significantly increasing time/token usage.
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The Role of Seniority Signals
Advanced review agents can use signals like reviewer seniority or expertise to boost the visibility of important past comments, ensuring critical institutional knowledge is surfaced during code reviews.
