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Context engineering — dynamically filling an AI model's context window with relevant information — is presented as a key source of competitive advantage. The framework covers three core components: knowledge (using RAG and graph-based representations to encode domain expertise), tools (encapsulating deterministic business logic as callable functions), and memory (storing session and cross-session state for personalization). Using a RevOps forecasting agent as a running example, the post explains how a 'context builder' orchestrates these components, why naive RAG often fails in enterprise settings, how tools should be documented and selected dynamically, and how memory types differ by persistence and scope. The interplay between all three components, plus the importance of collaboration between domain experts and engineers, is emphasized as the path to building AI systems that reflect how a business actually operates.

Context Engineering as Your Competitive Edge

Memory — the key to personalized, self-improving AI

Interactions between the three context components