Large Language Models are rapidly evolving from passive text generators into active decision making systems that can reason across complex inputs and support real world operations. However, many organizations still struggle to move beyond isolated use cases toward scalable, production ready AI systems that deliver consistent business value. This session introduces AI agents as decision co pilots powered by LLMs, enabling a shift from static analytics and rule based automation to adaptive, context aware intelligent systems. These agents continuously ingest and interpret diverse data signals, including structured operational data, unstructured inputs, and real time contextual information. By combining reasoning capabilities with domain awareness, they generate dynamic recommendations, evaluate scenarios, and communicate insights in natural language. The talk presents a practical three layer architecture for building agent driven systems. The prediction layer focuses on signal processing and model outputs, the decision layer translates insights into actionable recommendations, and the interaction layer enables seamless human collaboration through conversational interfaces. This approach allows organizations to move from fragmented tools toward unified decision systems that scale across functions and use cases. The session will also explore how LLM powered agents reduce cognitive load, improve decision consistency, and accelerate response times in complex environments. Rather than replacing human expertise, these systems augment decision makers by enabling faster synthesis of information and more informed actions. Attendees will gain a clear framework for designing, deploying, and scaling LLM driven AI agents in production, along with practical insights into building systems that are adaptable, explainable, and aligned with real world operational needs.
Mazdul Hassan Choudhury is a Product Management leader with over 15 years of experience delivering digital products across retail operations, supply chain, merchandising, fulfillment, last-mile delivery, and customer care. He has led the development and launch of enterprise products that improved operational efficiency, increased revenue, and enhanced customer experience. He currently serves as a Staff Product Manager at Walmart, where he owns and manages associate-facing applications and aligns product roadmaps with enterprise strategy. His work includes launching a GenAI-powered tool that improved operational efficiency, redesigning onboarding processes to reduce onboarding time by 80%, and delivering solutions that improved fulfillment accuracy and supported large-scale pre-order operations. Prior to Walmart, Mazdul worked at Bob’s Furniture, where he led the development of a mobile point-of-sale system used by 3,500 associates, increasing sales by 20% and reducing order processing time. He also built a customer relationship management platform that improved customer satisfaction and developed analytics solutions that increased operational visibility. Earlier in his career, he held roles at Capgemini, Equinor, and IBM, where he managed cross-functional teams and delivered SAP implementations, system integrations, and large-scale technology projects. He holds an MBA in Strategy and Operations and a Master’s degree in Information Systems Management from Boston University, along with a Bachelor’s degree in Electronics and Communication Engineering.
