AI systems are no longer limited to answering prompts. They are reading files, calling APIs, triggering workflows, searching internal systems, and orchestrating tools across environments. What began as simple model interaction has evolved into full agent execution. At the center of this transition is the Model Context Protocol (MCP) a framework that standardizes how AI agents connect to external tools and services. MCP is quickly becoming foundational infrastructure for agentic workflows. But as organizations move from experimentation to production, they encounter a new class of challenges that traditional AI stacks were never designed to solve. The issue is no longer just model performance. It is governance, visibility, and cost control across increasingly complex tool ecosystems.…