Multi-agent systems are gaining traction across various domains due to their ability to adapt and operate in dynamic environments while accomplishing complex tasks. This is largely attributed to their capabilities in tool invocation, context management, planning, and inter-agent collaboration. While their potential is promising, developing sophisticated multi-agent systems for real-world applications presents a unique set of challenges, including accuracy, latency, and operational costs.

In this talk, we will explore these challenges in depth and discuss practical strategies to address them. Performing a case study with a conversational AI assistant interacting with enterprise data, we will elaborate on how multiple agents were orchestrated to address dynamic computations and information retrieval from both structured and unstructured data sources to derive insights. We will walk you through the lessons we learned while building a production-ready multi-agent application.