Engineering Multi-Agent Coordination Patterns
Design patterns, protocols, and implementation strategies for building robust multi-agent systems.
This session explores the technical foundations of multi-agent systems, focusing on coordination protocols, inter-agent communication, and distributed decision-making. We examine architectural patterns for agent orchestration, state management across distributed agents, message-passing protocols, consensus mechanisms, and failure handling strategies. Topics include actor-model implementations, event-driven architectures, workflow engines for agent coordination, and patterns for scaling multi-agent systems. The discussion covers real-world implementation challenges, debugging distributed agent systems, and maintaining system coherence across autonomous components.
Overview
This session explores the technical foundations of multi-agent systems, focusing on coordination protocols, inter-agent communication, and distributed decision-making. We examine architectural patterns for agent orchestration, state management across distributed agents, message-passing protocols, consensus mechanisms, and failure handling strategies. Topics include actor-model implementations, event-driven architectures, workflow engines for agent coordination, and patterns for scaling multi-agent systems. The discussion covers real-world implementation challenges, debugging distributed agent systems, and maintaining system coherence across autonomous components.
Thank you for your interest in this talk. We look forward to seeing you!
Recommended Talks
Explore related talks that complement this research
TECHNICAL
Agents
Introduction to Agentic AI
A practical introduction to the core components required to build reliable, production-ready agentic AI systems.
TECHNICAL
Agents
Agent Architectures and Design Patterns
Design Patterns for Robust, Controllable Multi-Agent Systems
TECHNICAL
Agents
Vector Databases for LLM Systems: Foundations, Architectures, and Emerging Directions
A practical look at how vector databases power RAG systems, improve retrieval quality, and support real-world LLM applications.
TECHNICAL
Agents
Building AI Systems That Learn from Real Humans
A practical overview of how human feedback, cognitive biases, and behavioral models can improve AI alignment.