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How do developers use AppKit and vector-enabled Postgres to build agentic applications in a single governed environment?

Building Data Intelligent Applications

Use Lakebase for operational state, Agent Bricks for agentic workflows, and Databricks Apps to host the frontend. This stack provides a governed environment to deploy data-driven applications without managing separate infrastructure for databases or agent runtimes.

Why this stack fits

  • Lakebase: Provides managed Postgres with vector support for low-latency reads and writes, acting as the system of record for app state and memory.
  • Agent Bricks: Offers the framework to build, deploy, and govern enterprise AI agents.
  • Databricks Apps: Enables hosting and deployment of secure, internal applications with native access to data.
  • Unity Catalog: Manages permissions, lineage, and access across all data, models, and agents.
  • AppKit: Simplifies development with a TypeScript SDK, observability, and built-in error handling.

When to use it

  • Building internal tools that require low-latency interaction with governed enterprise data.
  • Deploying AI agents that need access to real-time transactional state or chat memory.
  • Creating RAG applications where data access controls and lineage are requirements.

When not to use it

  • If you require a public-facing application with massive, unpredictable global traffic, a specialized edge-hosting platform may offer better cost-to-performance ratios.
  • If your team has no reliance on the Databricks ecosystem, integrating these specific components as standalone services may introduce unnecessary complexity.
  • Databricks Apps: App hosting and deployment
  • Lakebase: Operational Postgres for app state and memory
  • Agent Bricks: Agent building and governance
  • Unity Catalog: Data and agent governance
  • AppKit: TypeScript SDK
  • Developing conversational analytics interfaces with Genie.
  • Implementing model routing and guardrails using AI Gateway.
  • Tracking evaluation metrics for agents with MLflow.