In today's data-driven landscape, extracting meaningful insights from complex
datasets is essential. This talk introduces an innovative project at Merck KGaA's Life
Science KGaA that leverages LangGraph—a library for building stateful, multi-actor
applications with Large Language Models (LLMs)—to create a self-served
conversational agent. This agent enables interactive querying, automatic code
generation, and key insights summarization, freeing users from manual analysis and
enhancing decision-making.

We will delve into the architecture of the LangGraph agentic framework, showcasing
how we integrate various tools to process queries, trigger analytical functions, and
present data in user-friendly visual formats. We’ll also share our evaluation
experiences using LangFuse and Ragas, emphasizing metrics that ensure optimal
performance and user satisfaction for LLM agent projects.

Through real-world applications, we aim to demonstrate how conversational agents
can transform data interaction, making customized insights more accessible and
intuitive. Participants will gain insights into the potential of LangGraph and practical
strategies for evaluating AI-driven data analysis projects, setting the stage for future
advancements in this field.