May 22
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Single AI agents are hitting a wall. Complex real-world tasks need browsing, coding, verification, and human oversight — simultaneously. Multi-agent orchestration is how AI became production-grade. In this video, I break down: • Why multi-agent systems emerged and the 5 forces that drove adoption • The 4 orchestration architectures: Supervisor, Graph, Swarm, and Debate • LangGraph vs CrewAI — enterprise workhorse vs hackathon champion • AutoGen, OpenAI SDK, and Google ADK: which ecosystem wins • The hidden failure modes demos never show you • Why MCP + A2A protocols matter more than any single framework • A practical decision guide: how to pick the right stack for your constraints For AI engineers, ML practitioners, and anyone building production AI systems in 2026. ───────────────────────────────────── 🕐 Chapters 0:00 – Hook: AI Is Becoming a Team Sport 0:35 – What Is Multi-Agent Orchestration? 1:12 – The 5 Forces That Drove Adoption 1:58 – The 4 Orchestration Architectures 2:42 – The 2026 Framework Landscape 3:28 – LangGraph vs CrewAI: Deep Dive 4:26 – AutoGen, OpenAI SDK and Google ADK 5:23 – The Big Debate: Do Frameworks Even Help? 6:03 – The Hidden Failure Modes 6:47 – Enterprise Reality vs Demo Reality 7:46 – Protocols Are the New Frameworks 8:29 – Choosing the Right Framework 9:28 – Key Takeaways ───────────────────────────────────── #MultiAgent #AIOrchestration #LangGraph #CrewAI #AIAgents #MachineLearning #AI #AgenticEngineering #GoogleADK #OpenAISDK #AutoGen #AgentOS