Multi-Agent Systems: Architectures for Next-Generation AI Solutions

Join Kansas City's Data Science community on September 11 to learn from Alfredo Castillo and Kevin Klug of Amazon Web Services about multi-agent systems.

The evolution of generative AI has brought forth sophisticated multi-agent systems (MAS) that can tackle complex tasks through distributed intelligence and collaborative problem-solving. These systems represent a paradigm shift from traditional single-agent AI implementations to coordinated networks of specialized agents working in concert. As institutions continue their digital transformation journeys, understanding the fundamental patterns of multi-agent architectures becomes crucial for designing robust, efficient, and scalable AI solutions. This session examines the core patterns in multi-agent systems—Parallel, Sequential, Loop, Router, Aggregator, Network, and Hierarchical. We'll explore how these architectures can revolutionize workloads through enhanced reasoning, planning, and collaborative intelligence.

Expected outcomes:

• Learn how to build agentic systems using multi-agent collaboration into your workloads
• Understand the different types of agentic collaboration patterns and their applications in use cases
• Gain practical knowledge of implementing multi-agent architectures that enhance decision-making capabilities
• Discover strategies for deploying scalable multi-agent systems that address complex workflows