MCP as the Integration Layer for AI Agents

Multi-agent AI systems often become difficult to scale because integration work shifts engineering effort away from agent logic and into custom API plumbing. The whitepaper explains how MCP can standardize how agents interact with tools, data platforms and external systems while keeping orchestration logic separate from execution logic. It also highlights practical production concerns: LLM-friendly tool design, scoped tool access, token budgets, observability, evaluation and security guardrails. Technology leaders should assess MCP as an integration architecture decision, not only as an AI development convenience.