2025-09-13 –, Track 1
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 delve into these challenges in depth and discuss practical strategies for addressing them through a detailed case study of a conversational AI assistant designed to interact with enterprise data. We will elaborate on the practices followed to build multi-agent systems that handle diverse tasks to derive insights.
Introduction (5 mins)
Challenges faced in making it Production-ready and solutions to address them(15 mins)
Conclusion (5 mins)
Question and Answer (5 mins)
Basic understanding of Python, APIs, and LLM/RAG concepts will be helpful. But if you have ever written a chatbot or SQL query, you’ll feel right at home!
Additional Resources – Target Audience –Intermediate
Sibin Bhaskaran is a Solution Consultant and technology lover at Sahaj AI Software. With over a decade of practical knowledge, he strives to gain a comprehensive understanding of the entire business lifecycle, putting into perspective the pivotal role that technology plays in its overall success. He has extensive knowledge across diverse domains of technology, spanning backend development, mobility solutions, robotic process automation, and machine learning.