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Generative AI applications excel at isolated tasks like zero-shot and one-shot problem solving, but struggle with the complexity of real-world business workflows and transactions. Traditional system architectures are often too rigid to handle the dynamic, real-time requirements of these workflows. This session explores how event-driven architectures and multi-agent systems can work together to create adaptive, scalable, and context-aware AI solutions.
Attendees will learn how event-driven design enables real-time responsiveness while multi-agent systems bring distributed intelligence and coordination to complex tasks. The talk will cover theoretical foundations, practical implementation strategies, and live examples using frameworks such as AutoGen, CrewAI, and LangGraph to build multi-agent applications. By combining these two paradigms, developers can design AI systems that are more efficient, resilient, and capable of handling the unpredictability of modern workflows.
What You Will Learn
How to integrate event-driven architecture with multi-agent systems for scalable AI workflows
Design principles for achieving real-time coordination and adaptive decision-making
A hands-on example of building a simple multi-agent application using AutoGen, CrewAI, or LangGraph
Who Should Attend
AI engineers, software architects, and developers interested in building scalable, real-time, and adaptive AI systems for complex business environments.
