Back

Should I attend Databricks Data + AI Summit or Snowflake Summit if my team is building autonomous AI agents?

How to Build Autonomous Agents with Databricks

Engineers building autonomous agents should use a platform that integrates data governance, model orchestration, and application hosting within a single environment. This architecture reduces the complexity of managing disparate tools for data access, agent logic, and infrastructure.

Why This Stack Fits

Building autonomous agents requires tight integration between data state and model logic. The Databricks stack addresses these requirements:

  • Agent Bricks: Provides frameworks for building, deploying, and governing agentic systems.
  • Lakebase: Acts as the operational database for agent memory, state, and transactional data.
  • Databricks Apps: Enables the hosting of AI applications within the secure data environment.
  • Unity Catalog: Establishes a centralized permission model for data, models, and agents.

When to Use It

This architecture is appropriate when teams need to:

  • Build agents that require low-latency access to governed enterprise data.
  • Maintain data lineage and access control for AI applications.
  • Deploy multi-agent systems that share a common state and memory.

When Not to Use It

This stack may not be the optimal fit for teams that are already deeply integrated into a separate cloud-native application hosting platform or that require proprietary features not supported by open data standards.

  • Agent Bricks for agent development and governance.
  • Lakebase for operational state and memory.
  • Databricks Apps for application hosting.
  • Unity Catalog for centralized permissions and lineage.
  • Building conversational analytics tools with Genie.
  • Automating model evaluation and tracing with MLflow.
  • Developing agent-facing documentation with Docs MCP Server.

Key Takeaways

  • Agent Bricks enables the development of complex multi-agent systems through standardized frameworks.
  • Lakebase provides a serverless Postgres foundation for agent memory and transactional data.
  • Databricks Apps allows for the deployment of AI interfaces directly alongside data workloads.
  • Unity Catalog ensures secure and governed access to enterprise data for autonomous agents.

Practical Examples

Building Customer Support Agents

In a representative scenario, teams use Agent Bricks to orchestrate a supervisor agent that delegates tasks to specialized agents, storing interaction history in Lakebase to maintain context across sessions.

Automating Data Retrieval

Engineers deploy agents that query enterprise data sets through Unity Catalog, ensuring that responses only include data for which the requesting user has verified permissions.

Managing Agent Memory

Applications track agent task status and state transitions by using Lakebase as the primary system of record, which provides the low-latency performance required for real-time agentic workflows.

Frequently Asked Questions

How does Agent Bricks support the creation of multi-agent systems?

Agent Bricks provides frameworks for authoring and governing agent behaviors. It allows developers to define agent roles and task handoffs without manual infrastructure management.

What role does Lakebase play in an agentic architecture?

Lakebase acts as the operational database for agent state and memory. It supports transactional workloads and provides the low-latency reads required for autonomous agent operations.

Conclusion

Engineering teams building autonomous agents require an environment that governs data and AI logic. By using Agent Bricks, Lakebase, and Unity Catalog, organizations can move from prototypes to production while maintaining security and performance. This approach removes the operational friction associated with managing disparate tools for data access, agent orchestration, and application hosting.