I Don’t Need to Know Databricks. An AI Agent Will Do It for Me.
No docs, no tutorials. Just Claude Code and a test dataset. Here’s what that tells us about the future of enterprise platforms.
Databricks just raised $7 billion at $5.4 billion in annual revenue and 65% growth. Impressive numbers, sure. But what caught my attention isn’t the fundraise but what the company has quietly become.
And more importantly, what that transformation tells us about where all enterprise software is heading.
From Data Warehouse to Operating System for AI
If you still think of Databricks as a data warehouse with dashboards, you’re looking at a two-year-old snapshot.
Today, Databricks is an infrastructure platform of an entirely new class. It’s a place where you can store and process data, host modern LLMs, talk to your data in natural language, and most critically, build agentic solutions. All under one roof.
For large enterprises, this is a fundamentally different category of system. We’ve been working with Databricks in enterprise projects at Customertimes for a while now, and we see this shift from the inside every day. The platform isn’t just evolving, it’s being rebuilt around a new paradigm.
The Real Difference: Architecture Built for Agents, Not Humans
Here’s the thing that separates Databricks from legacy platforms like SAP or Oracle.
Old enterprise systems were designed for humans clicking through menus. Screen by screen. Form by form. The user interface was the product. If you wanted to automate anything, you had to reverse-engineer the UI layer, build fragile integrations, and pray nothing broke on the next update.
Databricks is built differently:
API-first: every capability is accessible programmatically
Open architecture: no vendor lock-in traps, no proprietary black boxes
Agentic-ready: designed so that software can interact with it as a first-class user
This isn’t a minor technical distinction. It’s a philosophical one. The platform assumes its primary user might not be a person at all, it might be another program.
The Experiment: Zero Knowledge, Full Results
I recently ran an experiment that proved this point to myself.
I connected to Databricks through Claude Code, loaded a test dataset, and assembled dashboards with only surface-level knowledge of the platform. No documentation deep-dives. No tutorials. No certification courses.
Just an AI agent and a CLI.
The combination of agent + command-line interface genuinely changes the rules of engagement. I didn’t need to learn the platform’s UI conventions or memorize where settings live in nested menus. The agent understood the API surface, figured out the right calls, and got the job done.
This confirms something I’ve been thinking about for a while: modern systems win not because of beautiful UIs, but because of interfaces that agents can work with. The prettier your dashboard, the less it matters, if your API is solid, an agent can build whatever view a human needs on the fly.
Matching Technology to Your Stage
This also connects to something I’ve written about before: the importance of matching your technology choices to your company’s stage of development.
Databricks is a platform built for the AI era. Organizations that understand this gain an enormous advantage. They’re building their systems not on yesterday’s principles, but on the architecture of the future.
They’re not asking “what tool has the nicest interface?”. They’re asking “what platform gives my AI agents the most leverage?”
That’s a fundamentally different question, and it leads to fundamentally different decisions.
What’s Still Missing
Let’s be honest about the gap that still exists: documentation.
Almost every platform today, including Databricks, writes its docs for human developers. Step-by-step guides. Screenshots. UI walkthroughs. That’s fine for people, but it’s nearly useless for AI agents.
What agents need is different: clean API references, consistent schemas, predictable error handling, and machine-readable specifications. The platforms that figure this out first will have a massive adoption advantage.
But this is a matter of when, not if. Platform builders will catch on fast once they realize their main user is becoming software.
$7 Billion Says the Market Agrees
The $7 billion raise isn’t just an investment in a company. It’s a bet that all enterprise development and analytics will be built around AI. Databricks currently sits at the center of this new economy, offering not just a tool but an entire operating system for intelligent applications.
The market sees what’s coming:
Enterprise platforms will be agent-first, human-second
The value shifts from UI polish to API depth
The winners will be platforms that treat software as their primary customer
We’re at the beginning of this transition. Most enterprise software is still stuck in the old paradigm. But the signal is clear, and $7 billion of capital says the smart money agrees.
The Question for Your Business
Here’s what I’d challenge you to think about:
What platforms in your stack are agent-ready today?
Which ones could you hand off to an AI agent with a CLI and get meaningful results without deep platform expertise?
If the answer is “none” or “I don’t know,” that’s your starting point. The gap between agent-ready infrastructure and legacy systems is going to become the most important architectural decision of the next five years.
The platforms that are built for agents will compound in value. The ones that aren’t will become the new technical debt.
What platforms are you testing with AI agents? I’d love to hear what’s working and what’s not. Drop a comment or reply to this post.