Modern AI systems are moving beyond single LLMs toward multi-agent architectures that coordinate specialized agents for reasoning, research, and automation at scale. In this webinar, we’ll explore how production-ready AI systems use orchestration layers, supervisor models, parallel execution, and iterative workflows to improve reliability,...
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Modern AI systems are moving beyond single LLMs toward multi-agent architectures that coordinate specialized agents for reasoning, research, and automation at scale. In this webinar, we’ll explore how production-ready AI systems use orchestration layers, supervisor models, parallel execution, and iterative workflows to improve reliability, scalability, and performance. You’ll learn: Why solo AI agents fail at scale Core multi-agent design patterns Supervisor–worker and writer–critic workflows Parallel and recursive agent execution Common orchestration and debugging challenges Live demos will include parallel research agents, writer–critic loops, and a full deep research agent workflow. Perfect for AI engineers, LLM developers, teams building RAG systems, and anyone interested in scalable AI architectures.
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Last Updated: May 24, 2026
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