Navigating the Multi-Agent Trap
Learn about the risks that come with multi-agent systems, and how to reduce failure surface area, improve coordination and institute better cost controls.
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What You’ll Learn
- Why reliability math works against you at scale – and how to build better genAI systems
- The three most common architecture patterns – and why each breaks with genAI
- How to diagnose the most common genAI failure modes that standard tooling misses
- How to bridge the gap between infrastructure health and workflow correctness
Who Should Attend
This webinar is designed for engineering leads, AI/ML practitioners, and technical decision-makers responsible for deploying generative AI in production environments. If you’re building AI-powered features, managing model performance, or scaling inference workloads, this session will provide actionable frameworks you can implement immediately.
Whether you’re shipping your first AI feature or optimizing an existing system, you’ll leave with practical strategies to improve reliability, reduce costs, and accelerate iteration cycles.
Agenda
- 0:00 – 10:00Why multi-agent systems fail: Architecture problems that model quality alone can't fix
- 10:00 – 25:00Reliability math: How genAI errors compound across agent hops and why token costs follow the same curve
- 25:00 – 40:00Three production architecture patterns: When to use Plan-and-Execute, Supervisor-Worker and Swarm — and the exact conditions that break each one
- 40:00 – 52:00Five failure modes with traceable signatures: Compound decay, coordination tax, cost explosion, security gaps, and infinite retry loops
- 52:00 – 60:00Live Q&A: Bring your multi-agent architecture, a workflow that isn't behaving, or a design you're not sure about
Featured Speaker
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