Multi-agent systems & workflow orchestration: Why solo agents fail to scale
Modern AI systems are shifting from single-agent to multi-agent architectures for handling complex reasoning and automation tasks. Solo agents hit hard limits — they can’t parallelize work, they lose context over long tasks, and they can’t specialise. Multi-agent systems solve these problems with structured orchestration.
In this webinar, we’ll explore production-ready orchestration patterns using the deep-agents library, addressing the core limitations of standalone agents and demonstrating practical implementation strategies.
What You’ll Learn
- Why single AI agents fail at scale and where they break down
- Core multi-agent design patterns: supervisor-worker, parallel fan-out, and writer-critic loops
- How to build supervisor-based agent systems
- How to implement subagents using deep-agents
- How to design parallel and recursive workflows
- How to debug and observe multi-agent systems
Live Demos
- Parallel research agents working simultaneously
- Writer + critic loops for iterative refinement
- Full Deep Research Agent assembly with supervisor, researchers, writer, and critic
Who Should Attend
- AI engineers building complex agentic applications
- Developers exploring multi-agent orchestration patterns
- Teams moving from single-agent prototypes to production systems
- Anyone curious about why multi-agent architectures are replacing solo agents