Impossible Promise - Build UNLIMITED Context Agents: CrewAI Flows + Deep Research + MCP

Are your AI agents getting lost in their own context window? In this video, I break down a Hybrid Intelligent Automation Architecture that combines the structure of CrewAI Flows, the autonomy of Deep Agents, and the efficiency of Anthropic’s Model Context Protocol (MCP).
We explore how to decouple your process from your data using a "Zero-Content State" strategy—effectively giving your agents infinite memory without bankruptcy-level token costs.
📌 What You Will Learn:
• The Orchestration Layer: Using CrewAI Flows (@start, @listen) to manage the "Map" of your process.
• The Cognitive Layer: How Deep Agents use the file system (not context) to decompose complex tasks (write_todos) and spawn sub-agents.
• The Interface Layer: Implementing Anthropic’s "Progressive Discovery" with SKILL.md and efficient Code Execution via MCP.
• The "Pass-by-Reference" Rule: Why you should never pass raw text between agents.
💡 The Architecture Explained (The Library Analogy):
• CrewAI is the Librarian: They hold the Card Catalog (State/References) but don't memorize the books.
• Deep Agents are the Researchers: They go to the Stacks (File System), read books, and write notes in a notebook, returning only the notebook.
• Anthropic MCP is the Tool Room: Agents don't carry heavy tools; they "discover" them on the shelf only when needed via Progressive Disclosure.
🚀 Resources & Tools Mentioned:
• CrewAI Flows Documentation: [Link to source 3]
• Deep Agents (LangChain/LangGraph): [Link to source 35]
• Anthropic Model Context Protocol (MCP): [Link to source 66]
⏱️ Timestamps: 0:00 - The Context Window Trap 2:15 - Architecture Overview (Hybrid Model) 4:30 - CrewAI Flows & State Management 8:45 - Deep Agents & File System Memory 12:20 - MCP & Code Mode (Saving 98% Tokens) 15:00 - The "Public Library" Analogy 16:45 - Final Code Walkthrough
#AIAgents #CrewAI #Python #ModelContextProtocol #DeepResearch #LLM #LangChain #Automation