Auraa & Databricks

The Agentic Substrate for the Intelligent Lakehouse

Databricks gives you one of the most capable data platforms available. But infrastructure alone does not make decisions, detect anomalies, or coordinate workflows without someone maintaining the code that drives it.

Read the white paper

Key takeaways

No parallel permissions

Authorization runs against the Unity Catalog grants you already have.

Medallion, ACID

Bronze, Silver, Gold on managed Delta tables with Change Data Feed.

AIVARA plans, Jobs run

High-level intent becomes an execution plan delegated to Databricks Jobs.

Tool-first by design

DataDuct, AICURA, LEXARA, and AIREKA expose the platform as an API.

About this white paper

Mapped onto Databricks, not layered over it

Auraa is built exclusively on Databricks. It does not replace the primitives or route around them. It maps directly onto Delta Lake, Unity Catalog, and Jobs, then adds the orchestration, automation, and AI-driven abstractions that turn raw capability into an autonomous product.

Written by the team that built and operates the platform
Includes the trade-offs we accepted, not only the wins

Read the architecture before you build it.

Twenty minutes of reading that can save a migration. No form, no gate, straight to the PDF.

Read the white paper Book a demo

Auraa does not introduce a separate orchestration layer or route around Databricks. It maps each of its capabilities directly onto the primitives Databricks provides. Storage and incremental processing use Databricks-managed Delta tables with Change Data Feed for ACID-compliant updates. Governance and access control map directly to Unity Catalog grants, roles, and catalog structures. Orchestration delegates to Databricks Jobs. The result is that every capability Auraa exposes is backed by a Databricks primitive, with no parallel infrastructure to maintain alongside it.

AIVARA is Auraa's primary orchestration agent. It receives high-level intents (for example, onboard this data source, or run quality validation across these tables), decomposes them into an execution plan, and delegates the compute-intensive workloads back to Databricks Jobs without manual configuration. AIVARA operates at the planning layer: it decides what needs to happen and which tools handle each step, while the actual execution runs on Databricks infrastructure.

These are four specialized tools in Auraa's tool-first architecture. Together they transform Databricks from a code-execution environment into a modular, agent-ready API platform. Each tool exposes a specific category of platform operations as typed, discoverable, governed API calls that agents (including AIVARA and Genie Code) can invoke. The tool-first design means every platform capability has a defined interface, a known authorization requirement, and an audit trail entry, rather than being implemented as ad-hoc code executed on cluster compute.

Auraa maps its security model directly to Unity Catalog grants, roles, and catalog structures. There is no Auraa-specific permission system running alongside Unity Catalog. When an agent invokes a tool, the authorization check runs against the Unity Catalog grants already configured for that workspace. This means that access control for Auraa's agent operations is visible, auditable, and manageable through the same Unity Catalog interface as access control for human users and automated jobs.

Auraa follows the Bronze-Silver-Gold medallion architecture using Databricks-managed Delta tables at each layer. Change Data Feed is enabled on Delta tables to support incremental processing: rather than reprocessing entire datasets on each pipeline run, agents read only the changes since the last checkpoint. Because Delta provides ACID guarantees, partial writes from a failed pipeline run do not corrupt downstream layers. Each layer transition is governed by the same quality rules and audit logging that apply to business data.