Databricks is making a bold claim: AI agents will become the next generation system of record for enterprises, but only if companies have a solid data platform to power them. At its Data + AI Summit in San Francisco this week, the company unveiled Genie One, an "agentic coworker" designed to automate tasks across business operations.

Why Does Databricks Think Agents Need Data Platforms?

The core argument from Databricks CEO Ali Ghodsi centers on a simple insight: AI agents need context to be useful. Without access to clean, organized data about a company's operations, customers, and workflows, agents can't make intelligent decisions or take meaningful action. Ghodsi framed the future enterprise software stack as fundamentally different from today's landscape.

"What does the agent system of record look like? It's actually the data and AI platform," stated Ali Ghodsi, CEO of Databricks.

Ali Ghodsi, CEO at Databricks

This positioning puts Databricks in direct competition with Snowflake, which held its own conference two weeks prior. Both companies are racing to become the infrastructure layer that enterprises rely on as AI agents move from experimental projects into production workflows. The stakes are significant: whoever controls the data and AI platform could become as essential to enterprises as software-as-a-service (SaaS) applications are today.

What Makes Genie One Different From Other AI Agents?

Genie One is positioned as an "agentic coworker" rather than a standalone tool or chatbot. This framing suggests the agent is designed to work alongside humans, automating routine tasks while leaving strategic decisions to people. The agent's ability to tap into enterprise data, applications, and workflows is meant to make it contextually aware in ways that generic AI models cannot be.

Databricks' broader strategy involves positioning itself as the connective tissue between AI agents and the enterprise systems they need to operate effectively. This includes not just data storage and processing, but also the ability to unify operational and analytical data, a capability Databricks emphasized at the summit.

How to Prepare Your Enterprise for Agentic AI Systems

• Audit Your Data Infrastructure: Evaluate whether your current data platform can provide clean, organized, and accessible data to AI agents. Fragmented or siloed data will limit what agents can accomplish.
• Identify High-Impact Use Cases: Start with workflows where agents can deliver immediate value, such as customer service automation, data analysis, or routine administrative tasks that consume employee time.
• Plan for Integration: Ensure your data platform can connect to the applications and systems your agents will need to interact with, from CRM tools to financial systems to internal databases.
• Establish Governance Frameworks: Define who can authorize agent actions, what data agents can access, and how to monitor agent behavior for compliance and security.

The broader tech industry is moving in this direction. Salesforce announced it is acquiring Fin, a customer service automation startup, for $3.6 billion, signaling that major enterprise software vendors see AI agents as a critical capability. Meanwhile, startups focused on agent security, governance, and deployment are raising hundreds of millions in funding, indicating investor confidence that agentic systems will reshape enterprise software.

What Does This Mean for the Future of Enterprise Software?

If Databricks' vision proves correct, the enterprise software landscape will shift from a collection of specialized tools (email, CRM, accounting software, analytics platforms) to a unified system where AI agents orchestrate work across all these applications. The data platform becomes the foundation that makes this orchestration possible.

This shift has profound implications for how companies organize their technology spending and how employees work. Rather than employees switching between multiple applications, they might interact primarily with an AI agent that handles routine tasks and surfaces information from across the enterprise. The data platform, in this model, is as critical as the operating system on a computer.

Databricks' timing is strategic. The company is not pursuing a traditional initial public offering, but it is making a public case for its importance to the AI era. By positioning itself as the infrastructure layer for agentic AI, Databricks is attempting to secure a position similar to what cloud infrastructure companies like Amazon Web Services and Microsoft Azure hold today. The Data + AI Summit served as a declaration of intent: Databricks intends to be central to how enterprises adopt and deploy AI agents at scale.