How to Generate Mergeable Code with a Context Engine — Peter Werry, Unblocked
Summary
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
-
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.
-
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.
-
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.
Technical details
-
Context Engine Functionality
729s
The engine synthesizes organizational context by ingesting data from multiple sources (e.g., Slack, Notion architecture documents, GitHub history/PRs). It can generate artifacts, such as architecture diagrams, that did not previously exist and provides 'show your work' traceability.
-
Code Review Automation
The system analyzes PR data alongside historical conversations to identify best practices. It can automatically surface past discussions related to a specific code change, correlating the fix with its original context.
-
Open Source Tools
Two open-source projects are available: a Document Query Engine (runs over GitHub history to synthesize schemas for querying) and an Engineering Social Graph (visualizes team review relationships and identifies coverage gaps in the codebase).
Mentioned resources
- Unblocked Context Engine
- Document Query Engine
- Engineering Social Graph
Channel & topics
Watch on YouTube · Back to latest
This independent, AI-assisted summary is provided for commentary and informational purposes. It may contain errors or omit important context. Please watch the original video for the creator's complete presentation. Video, thumbnail, and related copyrights belong to their respective owners.