Why AI agents need a Context Lake

Enterprise AI agents are running into three scaling bottlenecks: security approvals, context-window overload, and weak accuracy without basic company knowledge.

The article argues that simply adding more tools and MCP servers is not enough for enterprise use.

Instead, companies need a Context Lake: a shared, governed layer of curated organizational context such as ownership, dependencies, and internal definitions.

That shared context would let agents query reliable company knowledge at scale, rather than depending on ad hoc repo-by-repo instructions like AGENTS.md.