Solving Healthcare Claims Bottlenecks with Agentic Postgres Architectures

Revenue Cycle Management (RCM) and medical claims processing in the Healthtech industry are historically broken. Brittle legacy architectures suffer from slow manual intake, silent data bottlenecks, and high administrative error rates—ultimately harming the general consumer through delayed care and unexpected billing disputes. While AI agents offer a solution by automating intake and delivering real-time patient insights, their success hinges on something LLMs can't provide on their own: persistent long-term memory, rich context, and robust transactional state management.

In this session, we share a production-focused architecture demonstrating how standard Postgres semantics serve as the definitive anchor for modern, agentic AI workloads. We showcase a real-world Healthtech RCM application built using AI-driven development and the architecture behind agentic AI systems — combining the Databricks Lakehouse for governed, large-scale data processing with Lakebase (Serverless Postgres) for transactional, low-latency state — enabling AI agents to reason, act, and continuously learn in production workflows.

We will detail how a single Postgres engine eliminates the need for a fragmented architecture (vector DBs, caches, and separate operational stores) by executing four simultaneous roles: an app transactional backbone, a LangGraph short- and long-term memory store, a zero-ETL lakehouse data sync, and an atomic compliance audit log. Attendees will leave with practical, application-developer best practices for building, optimizing, and scaling agent-centric database schemas.

What Attendees Will Learn

• Architectural Blueprints: How to design a multi-tenant Postgres schema optimized for AI agent state, memory retention, and fast contextual retrieval.
• Healthtech Solutions: Concrete examples of how agentic AI and Postgres can directly automate manual data entry and pinpoint processing bottlenecks in regulated industries.
• Developer Velocity: Best practices for using modern AI developer toolkits (AI DevKit) to accelerate Postgres application delivery while maintaining strict database design standards.