Multi-Agent Systems & Workflow Orchestration: Why Solo Agents Fail to Scale

Modern AI systems are rapidly shifting from single-model solutions to agentic and multi-agent architectures that distribute tasks across specialized agents. This approach improves reliability, scalability, and performance by reducing context overload and enabling structured coordination between agents.
In this webinar, we explore how multi-agent systems and orchestration layers are used to build production-ready AI workflows for reasoning, research, and automation.

You’ll learn how modern systems coordinate multiple agents through supervisor models, parallel execution, and iterative refinement to solve complex tasks more effectively than standalone models.
We’ll also cover key design patterns, including supervisor–worker systems, writer–critic loops, and parallel agent workflows, along with practical strategies for building and debugging scalable agent systems.

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## What You Will Learn

• Why single AI agents fail at scale
• Core multi-agent architectures used in production systems
• How supervisor-based orchestration works
• Designing parallel and recursive agent workflows
• Implementing and scoping subagents effectively
• Common failure modes and how to debug them

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## Live Demos

• Parallel research agents working on sub-tasks simultaneously
• Writer + critic loop for iterative improvement
• End-to-end deep research agent with supervisor, researchers, writer, and critic agents

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## Who Should Attend

• AI engineers and LLM developers
• Teams building agentic workflows or RAG systems
• Researchers exploring multi-agent architectures
• Anyone interested in scalable AI system design

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## Outcome

By the end of the session, you’ll understand how to design, orchestrate, and scale multi-agent systems for real-world AI applications, with practical patterns you can apply directly in production workflows.