The "AI Coordination Gap" refers to the compounding reliability loss occurring during handoffs between agents, tools, and systems, rather than within the models themselves. A critical technical reality is that a six-step pipeline with 97% per-step accuracy yields only 83% end-to-end reliability. To mitigate this, engineers must implement a six-layer coordination stack: an Intent Layer using LangGraph for typed state objects, an Orchestration Layer (e.g., AutoGen or CrewAI) for task routing, a Context Layer using RAG via vector databases like Pinecone for real-time grounding, a Tool Layer utilizing Anthropic's Model Context Protocol (MCP) to ensure idempotent and typed tool calls, a Verification Layer employing critic agents to validate outputs, and an Observability Layer using LangSmith for trace-based debugging. Research indicates that while upgrading models offers marginal reliability gains (often less than 4%), implementing coordination layers can improve end-to-end reliability by 30–50%. As the agentic AI market is projected to reach $227B by 2034 with a 45.8% CAGR, moving beyond model tuning toward structured orchestration—such as using n8n for deterministic plumbing and MCP for standardized tool interfaces—is essential for production-ready deployments.