The five layers of agentic context

Effective AI agents require business-specific context beyond a model’s general knowledge, and this context can be organized into five distinct layers: canon, or approved facts, policies, and brand claims; records of past transactions and interactions; process rules defining authority and approvals; situational details from an active interaction; and memory of lessons learned across interactions. The piece argues that treating all of this as a single “company brain” risks creating unreliable, unowned information stores, since the layers have different owners, update rates, and governance needs. It places particular emphasis on canon as the most important safeguard for customer-facing agents, proposing that it should be managed through structured content operations involving drafting, review, approval, maintenance, and clear ownership rather than simple document retrieval. Content teams are positioned as central to resolving contradictions and maintaining this authoritative knowledge, while developers should provide tools that support the workflow. The author presents Sanity Knowledge Bases and workflows as infrastructure for compiling vetted canonical information from multiple sources, detecting conflicts, routing approved context to agents through MCP, and enabling smaller, less expensive models to produce more reliable responses under human oversight.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.