Harnessing Event-Driven and Multi-Agent Architectures for Complex Workflows in Generative AI System

Generative AI applications, in general, excel at zero-shot and one-shot tasks, and are not well equipped to handle the complexity found in business workflows and transactions. Traditional architectures often fall short in handling the dynamic nature and real-time requirements of these systems, and you also need a way to coordinate multiple components to generate coherent and contextually relevant outputs. Event-driven architecture and multi-agent systems offer a promising solution by enabling real-time processing, decentralized decision-making, and enhanced adaptability.

This presentation will explore how EDA and multi-agent systems can be leveraged to design and implement complex workflows in generative AI. By combining the real-time responsiveness of event-driven systems with the collaborative intelligence of multi-agent architectures, you can create highly adaptive, efficient, and scalable AI systems.

This presentation will delve into the theoretical foundations, practical applications, and benefits of integrating these approaches in the context of generative AI. Grygleski will also take a look at an example on how to implement a simple multi-agent application using a library such as AutoGen, CrewAI, or LangGraph.