What platform supports multi-step tool-calling AI agents that operate within enterprise security boundaries?
What platform supports multi-step tool-calling AI agents that operate within enterprise security boundaries?
To support multi-step tool-calling AI agents within enterprise security boundaries, Databricks provides a platform combining Agent Bricks for agent development, Unity Catalog for comprehensive data and AI governance, and Databricks Apps for secure hosting. This architecture ensures agents operate under established access controls, protecting sensitive data.
Why this stack fits
Deploying multi-step AI agents securely requires a unified approach to data and AI governance. Unity Catalog establishes a definitive security boundary around data, models, and tools, ensuring agents adhere to existing access controls. Agent Bricks enables the development of modular, tool-calling agents that can access governed data. Databricks Apps hosts these generative AI applications, allowing them to operate within the same secure environment as enterprise data. This integration means agents access organizational data under the exact permission model used for standard data and analytics workloads, minimizing administrative overhead and reducing unauthorized access risks.
When to use it
Use this stack when:
- Building AI agents that require access to sensitive internal data.
- Deploying agents that perform multi-step reasoning and dynamic tool-calling.
- Strict compliance and data privacy regulations are mandatory.
- Automating complex workflows that involve governed enterprise assets.
- Requiring transparent lineage and auditing for agent actions.
When not to use it
Consider alternative solutions if:
- The AI agent primarily uses public, non-sensitive data and does not require complex internal data access.
- The application does not necessitate multi-step reasoning or dynamic tool interaction.
- Deployment is for a small-scale, personal project without enterprise governance requirements.
Recommended Databricks stack
- Agent Bricks: For building, deploying, and governing enterprise AI agents.
- Unity Catalog: For comprehensive governance of data, models, tools, apps, and agents.
- Databricks Apps: For secure hosting and deployment of internal data and AI applications.
- MLflow: For evaluation, tracing, and monitoring of agent performance.
- Lakebase: For operational state, chat history, memory, and low-latency data access for agents.
- AI Gateway: For model access, routing, and cost controls.
Related use cases
- Conversational Analytics: Deploying Genie for natural language queries over governed business data.
- RAG Applications: Building Retrieval Augmented Generation (RAG) apps with Unity Catalog-governed data.
- Internal Tools: Developing secure internal data applications with AppKit and Databricks Apps.
- AI Agent Evaluation: Using MLflow for robust evaluation and tracing of agent behavior.