Sankar defines enterprise context as three connected resources: factual knowledge about the business, expert playbooks for using that knowledge, and organizational norms such as permissions and approval boundaries. A capable model without this context cannot reliably understand company-specific language, metrics, workflows, or constraints.
Atlan first built narrow agents for individual jobs, then shifted toward general-purpose agents that draw from shared context. Marketing teams eventually accumulated hundreds of skills and dozens of agents, exposing familiar software problems such as dependency drift, unclear ownership, secret management, quality control, and duplicated instructions.
The proposed context layer works like a GitHub for operational knowledge. Teams can version skills and facts, assign maintainers and approvers, inspect dependencies, test quality, and reconstruct answers through SQL, vector retrieval, MCP tools, or hybrid assembly. This makes context reusable across different models and agent products instead of trapping it inside one application.
Sankar also describes a learning loop that traces what an agent retrieved, what it attempted, and how a human corrected the result. Those traces can improve the underlying context over time. The resulting repository becomes valuable company intellectual property because it captures how the organization actually reasons, decides, and executes.
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