Harnessing Event-Driven and Multi-Agent Architectures for Complex Workflows in Generative AI System
Generative AI applications, in general, excel in zero-shot and one-shot types of specific tasks. However, we live in a complicated world and we are beginning to see that today’s generative AI systems are simply not well equipped to handle the increased complexity that is found especially in business workflows and transactions. Traditional architectures often fall short in handling the dynamic nature and real-time requirements of these systems. We will also need a way to coordinate multiple components to generate coherent and contextually relevant outputs. Event-driven architectures and multi-agent systems offer a promising solution by enabling real-time processing, decentralized decision-making, and enhanced adaptability.
This presentation proposes an in-depth exploration of how event-driven architectures 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, we 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. We 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.
#GenAI #EventStreaming #DataPipeline #AIAgent #MultiAgentic #Workflows
