A common scaling bottleneck is not the model. It is connection.
AI agents can only deliver outcomes when they can access the same tools and systems employees rely on.
Enterprises have many systems. If every agent requires custom integration to every tool, integration work grows quickly and becomes expensive, fragile, and hard to scale.
Model Context Protocol (MCP) is emerging as a standardized way to wrap enterprise systems in a format agents can understand and use.
It functions like an API layer for the agentic era. Instead of teaching each agent how each system works, organizations expose capabilities once and reuse them across agents.
Models are probabilistic and have knowledge cutoffs. MCP connects agents to authoritative systems of record, which improves accuracy.
More importantly, MCP enables agents to take actions in enterprise systems, not only answer questions.
Use this bonus page as an orientation to the integration layer that often determines whether agents remain experiments or become scalable systems.