Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked
AI agents require more than just access to information; they need a 'context engine' to achieve true understanding of a company's operations, business logic, and historical decisions. The talk details how agents fail when they lack deep, organizational context, leading to wasted tokens and time. The solution involves building a context engine that ingests data from disparate sources (e.g., ticketing systems, code repos) and provides structured, conflict-resolved context to the model in a token-optimized manner.
Key takeaways
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The Context Gap
AI-generated code must feel like it was written by a long-term team member, which requires deep operational context that current agents lack. This gap compounds as agents scale from simple tab completion to background processes.
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Failure Modes of Context
7:00
Common approaches like the 'curated context trap' (where documentation rots) and the 'MCP plateau' (where agents suffer from satisfaction of search bias) fail because access to information is not the same as understanding it.
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The Role of the Context Engine
9:12
A proper context engine must perform conflict resolution (e.g., resolving conflicting architecture diagrams vs. Slack messages), respect permissions (OAuth/SSO), and deliver context in a token-optimized way.
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Efficiency Gains
13:00
Implementing a context engine can lead to significant efficiency gains, demonstrated by a reduction from 21 million to 10.8 million tokens and a two-hour reduction in wall-clock time for the same task.