Day 1

Traditional revenue management and marketing science models are increasingly throttled by Human-in-the-Loop bottlenecks and search space explosion where the exponential growth of variables (cross-elasticity, competitor tactics, and channels etc.) and the number of models outpaces manual oversight. Static ML workflows simply cannot scale when every scenario requires human-led iterative corrections.

This session explores the agentic shift where classical ML models are wrapped within a reasoning loop. By treating models as “Tools” rather than final outputs, LLMs act as a central “brain” to decompose high-level goals into executable plans. These systems utilize long-term memory to retain historical context and self-correcting loops to autonomously refine model parameters or flag data anomalies. Unlike isolated model outputs that require human intervention to stay relevant, the autonomous system can perform diagnostic sub-routines, run multiple what-if and optimization scenarios, and automate modeling activities at scale.

As AI agents move into real-world use, the data layer becomes critical to their success. Traditional databases struggle with the real-time, high-concurrency, and context-heavy demands of agentic systems.

We will move past the hype of generative AI and dive into the technical frameworks that allow machines to navigate the real world with intelligence and autonomy.

This talk walks through the real-world journey of building a production-grade Agentic Financial Assistant—from early proof-of-concept experiments to a scalable, reliable system serving real users.

We will explore how large language models evolve into autonomous agents capable of reasoning, retrieving financial context, executing tools, and making safe decisions within enterprise constraints.

The session will cover practical architecture patterns including multi-agent orchestration, tool execution layers, memory and retrieval systems, and LLMOps for monitoring and reliability. We will also discuss the critical production challenges—latency, cost control, hallucination mitigation, guardrails, and evaluation frameworks—and how to design agent systems that are safe enough for financial environments.

Developers will walk away with practical blueprints for building agentic systems that move beyond demos into production.