Dialogues with Data: Accelerating Pre-Sales Discovery through Agentic AI

Pre-sales Solution Architects (SAs) lose momentum navigating enterprise information silos. At Databricks, hyper-growth GTM and a fast-changing product stack amplify the problem. We built an Agentic Research Assistant that turns internal GTM knowledge into a conversational solution-design engine aligned to real pre-sales workflows.This session is a technical deep dive into the pipeline: a streaming event-driven architecture integrated with Databricks Model Serving, MLflow, and Lakehouse-native state management for real-time adaptability and observability.Highlights Workflow-to-tool modeling for field-aligned agent actions Lakehouse state store for conversation memory + tool-call trajectories Intent-aware retrieval with Instructed Retrievers Conversational reasoning using a DSPy ReAct extension Continuous optimization with DSPy + GEPA from historical trajectoriesA blueprint to move beyond basic RAG to self-improving agents that accelerate technical discovery and solutioning.