MCP Alone Is Not Enough: Enterprise AI Platforms Need Seven Distinct Boundaries

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The Model Context Protocol (MCP), open-sourced by Anthropic in November 2024, has become the go-to answer for enterprise AI agent architecture, but experts argue it only solves one of several critical boundaries. MCP standardizes how AI clients discover and invoke tools, yet it does not handle authorization, business transactions, durable processes, or outcome evaluation. Alongside MCP, three other open protocols have emerged within roughly 18 months: Google's A2A for cross-vendor agent delegation, CopilotKit's AG-UI for streaming agent-to-frontend connections, and AWS AgentCore for managed agent identity and runtime infrastructure. A robust enterprise AI platform requires at least seven distinct boundary layers, covering agent-to-tool, agent-to-service, agent-to-agent, agent-to-user, service-to-service, durable process management, and identity and authorization. As AI models grow more capable and attempt more autonomous actions, the need for strong control, audit, and governance layers becomes more critical, not less.

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