Building Scalable Multi-Agentic AI Systems: Orchestrating Agents with Event-Driven Approach

Multi-agent AI gets harder when agents need to coordinate at scale. Coordination patterns matter once agents have to share state, communicate, and keep work moving across a system. Mary Grygleski uses "Building Scalable Multi-Agentic AI Systems: Orchestrating Agents with Event-Driven Approach" to follow that thread into event-driven patterns for communication, memory, workflow state, and MCP, giving attendees a practical way to bring the lesson back to their own systems.

Session details

This talk will guide developers through the design and implementation of multi-agent generative AI systems using event-driven principles. Attendees will learn how autonomous GenAI agents collaborate, communicate, and adapt in real-time workflows using modern frameworks (such as LangChain or Haystack) and messaging protocols. Cover core patterns such as pub/sub, orchestrator, and supervisor for managing agent communication, memory, and workflow state. Discuss interoperability with MCP (Model Context Protocol) and how event streaming tools like Kafka, Pulsar, that can facilitate high throughput and reliability.

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Agenda facts

Format
Talk / Presentation
Kind
Conference Session
Topic
Artificial Intelligence and Machine Learning
Level
Intermediate
Time
Oct 30, 2026, 1:15 PM
Room
Room F
Duration
60 min

Speakers

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LinkedIn

Session LinkedIn Post

Multi-agent AI gets harder when agents need to coordinate at scale. Coordination patterns matter once agents have to share state, communicate, and keep work moving across a system. Mary Grygleski uses "Building Scalable Multi-Agentic AI Systems: Orchestrating Agents with Event-Driven Approach" to follow that thread into event-driven patterns for communication, memory, workflow state, and MCP, giving attendees a practical way to bring the lesson back to their own systems. 📅 October 28–30, 2026 📍 Oakland University, Rochester, MI 🔗 https://www.mitechcon.net/sessions/building-scalable-multi-agentic-ai-systems-orchestrating-agents-with-event-driven-approach/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-building-scalable-multi-agentic-ai-systems-orchestrating-agents-with-event-driven-approach #MITechCon #AIAgents #EnterpriseAI #AIArchitecture

MediumUpdated 2026-07-08
Short social

Short Session Post

Multi-agent AI gets harder when agents need to coordinate at scale. Learn event-driven patterns for communication, memory, workflow state, and MCP. https://www.mitechcon.net/sessions/building-scalable-multi-agentic-ai-systems-orchestrating-agents-with-event-driven-approach/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-building-scalable-multi-agentic-ai-systems-orchestrating-agents-with-event-driven-approach #MITechCon #AIAgents

ShortUpdated 2026-07-08
Newsletter / email

Newsletter Or Email Blurb

Feature this MITechCon 2026 session in your newsletter or email: "Building Scalable Multi-Agentic AI Systems: Orchestrating Agents with Event-Driven Approach". Multi-agent AI gets harder when agents need to coordinate at scale. Coordination patterns matter once agents have to share state, communicate, and keep work moving across a system. Mary Grygleski uses "Building Scalable Multi-Agentic AI Systems: Orchestrating Agents with Event-Driven Approach" to follow that thread into event-driven patterns for communication, memory, workflow state, and MCP, giving attendees a practical way to bring the lesson back to their own systems. Format: Conference Session. Topic: Artificial Intelligence and Machine Learning. Level: Intermediate. Scheduled for Oct 30, 2026, 1:15 PM in Room F. Learn more and share the session: https://www.mitechcon.net/sessions/building-scalable-multi-agentic-ai-systems-orchestrating-agents-with-event-driven-approach/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-building-scalable-multi-agentic-ai-systems-orchestrating-agents-with-event-driven-approach

MediumUpdated 2026-07-08

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Michigan technology communityAI and machine learning teamsData and product leaders

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