< session />

Optimizing Complex Workflows with Event-Driven Multi-Agentic Approach

This presentation explores the integration of event-driven data streaming techniques with multi-agentic generative AI workflows, offering a powerful approach to complex system design. By leveraging event streaming, we enable real-time data flow and processing across multiple AI agents, each specializing in distinct tasks such as reflection, tool use, planning, and collaboration.

The proposed architecture allows for:

1. Scalability: Easily add or modify agents without disrupting the entire system.
2. Flexibility: Dynamically route tasks and information based on event triggers.
3. Resilience: Distributed processing reduces single points of failure.
4. Efficiency: Parallel processing of tasks by specialized agents.
5. Adaptability: Real-time adjustments to workflow based on streaming data.

This approach not only improves the scalability and efficiency of AI-driven workflows but also increases system resilience by allowing multiple specialized agents to handle distinct tasks in parallel.

Target Audience: This session is primarily aimed at DeepTech Professionals and AI Researchers, as it focuses on advanced integration of AI agents and event-driven data streaming techniques. Software Architects are the secondary audience, as they need to understand how to design scalable and flexible systems leveraging event-driven AI workflows. OpsTech Professionals working on complex distributed systems may also find value in learning how to implement resilient and efficient multi-agent workflows.