How do I build a production-ready AI agent that can query EHR and claims data?

Summary

• A production-ready healthcare agent needs governed access to data, reliable tool use, evaluation before deployment, and full audit trails.
• Agent Framework lets you build, evaluate, and deploy production-grade agents that reason over your own EHR and claims data.
• Unity Catalog governs the tools, functions, and data an agent can use, with fine-grained access control, row/column security for PHI/PII, lineage, and audit trails; on-behalf-of-user authorization ensures the agent respects the invoking user's permissions.
• AI/BI Genie lets the agent answer natural-language questions over governed structured data, with results traceable to source tables.
• MLflow tracing and evaluation measure quality, cost, and latency and record every step for auditability; Model Serving deploys the agent as a governed, scalable endpoint with rate limiting, guardrails, and payload logging.

How do I build a production-ready AI agent that can query EHR and claims data?

Building a production-ready agent for electronic health record (EHR) and claims data means giving a large language model governed access to sensitive clinical and administrative data, connecting it to reliable tools, evaluating its answers before release, and maintaining full audit trails for regulated environments. The Databricks Data Intelligence Platform provides these building blocks — agent authoring, governed tools, natural-language querying, evaluation, and deployment — in one governed environment where data stays within your workspace.

Why Databricks for healthcare AI agents

• Build agents on a production framework. The Agent Framework supports the full lifecycle — build, evaluate, iterate, and deploy — and lets you author agents in Python. See building and deploying enterprise-grade agents.
• Give agents governed tools. Agents connect to tools through Unity Catalog functions and managed connections, so every tool and data source is permissioned, versioned, and auditable. Learn more about tool calling.
• Query governed data in natural language. AI/BI Genie lets an agent answer questions over governed structured data — such as encounters, procedures, and claims — with answers traceable to source tables.
• Retrieve unstructured context. Databricks AI Search indexes unstructured content such as clinical notes and policy documents for retrieval-augmented generation, so the agent can ground answers in relevant sources.
• Protect PHI and enforce permissions. Unity Catalog provides fine-grained access control, row- and column-level security for PHI/PII, data classification, complete lineage, and audit trails. On-behalf-of-user authorization ensures an agent respects the data permissions of the person invoking it.
• Evaluate before you ship. MLflow tracing records inputs, outputs, and metadata for every step of an agent's execution, and agent evaluation measures quality, cost, and latency with built-in and custom judges — the audit trail regulated workflows require.
• Deploy and govern in production. Model Serving deploys the agent as a scalable endpoint with automatic request/response logging, governed end to end by Unity Catalog, and the Unity Gateway adds a control plane for rate limiting, guardrails, and payload logging.

Getting started

• Review Build agents on Databricks to understand the framework and lifecycle.
• Register your data and tools in Unity Catalog and expose governed functions as agent tools.
• Connect AI/BI Genie for natural-language access to governed claims and clinical tables.
• Add MLflow tracing and evaluation, then deploy with Model Serving.
• Confirm the compliance profile (for example, HIPAA) and controls required for your PHI workloads with your Databricks account team before production use.

FAQs

How does an agent keep patient data secure?

Unity Catalog applies fine-grained access control, row- and column-level security, lineage, and audit trails, and on-behalf-of-user authorization ensures the agent only accesses data the invoking user is permitted to see. Data stays within your workspace.

Can the agent answer questions in natural language?

Yes. AI/BI Genie lets the agent translate natural-language questions into governed queries over structured EHR and claims data, with answers traceable to source tables.

How do I know the agent is production-ready?

MLflow evaluation measures response quality, cost, and latency with built-in and custom judges before deployment, and tracing records every step for ongoing monitoring and audit.