Mergeable by default: Building the context engine to save time and tokens — Peter Werry, Unblocked
TL;DR
Peter Werry argues that as AI agents move toward autonomous 'YOLO mode' execution, simple RAG and MCP connections fail to provide adequate organizational context, creating bottlenecks and 'satisfaction of search' failures where agents stop at superficial answers instead of understanding the historical 'why' behind code decisions.
🔄 The Human Bottleneck Problem 3 insights
Humans become cognitive bottlenecks
With parallel agents and YOLO mode execution, engineers cannot manage the context switching required to manually feed information to multiple simultaneous background processes.
Organizational knowledge requires battle scars
True context includes institutional memory of incidents, outages, and historical decisions—not just current code state—enabling agents to understand why systems work the way they do.
Background agents are inevitable
As code intelligence reaches exponential improvement, the limiting factor becomes context delivery, requiring engines that can operate autonomously without human intermediaries.
❌ Three Myths of Context Provision 3 insights
Naive RAG causes satisfaction of search
Simple vector search leads agents to stop at first findings (like radiologists missing secondary issues), overlooking critical context buried in Slack threads or incident reports.
MCP connections lack understanding
Wiring up data sources provides access but fails to reveal relationships between systems, historical motivations for changes, or the reasons behind architectural decisions.
Larger context windows don't solve reasoning
Even million-token windows cannot fit entire organizational contexts, and size doesn't help agents determine truth versus outdated information or reason across disparate sources.
🏗️ Building a True Context Engine 3 insights
Prevent satisfaction of search
Engines must surface previously rejected solutions, analyze deletion history, and understand user intent rather than stopping at the first compiling code solution.
Resolve conflicts beyond recency
Truth determination requires identifying contradictions between documentation and code, recognizing that main branch isn't always the future source of truth, and learning from user corrections.
Enforce access controls at the core
Context engines must respect permissions like private Slack channels, ensuring sensitive information only surfaces for authorized users while maintaining strict privacy boundaries.
Bottom Line
Organizations must build context engines that resolve conflicts between data sources, preserve institutional knowledge of past failures, and enforce access controls to prepare for autonomous background agents that understand the 'why' behind decisions, not just the 'what' of current code.
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