Your agents lack context: Here's how to fix "You're absolutely right!" — Brandon Waselnuk, Unblocked
Brandon Waselnuk from Unblocked demonstrates that AI agents often lack contextual understanding of an organization, leading to wasted computing resources and slow processes.
He shows that the same prompt without a context engine consumed 21 million tokens and took two hours longer than with a context engine that used only 10.8 million tokens. The problem grows when teams scale from simple code completion to parallel agents—agents begin making errors, searching through information inefficiently, and requiring many review rounds. Waselnuk argues that simply giving agents access to information is insufficient; they need a proper context engine that can resolve conflicts, understand what is relevant for each specific team member, and maintain permissions.
Vibekollen prepared this summary with AI from the original publication. The content belongs to AI Engineer.
