What Early Access to Databricks’ Agent Bricks Taught Us — and Why It Matters for Your Business

Agent Bricks, Databricks’ new suite of AI solutions, was recently announced during the Databricks Data + AI Summit and is currently available to a limited set of customers. But before there was a launch, a UI, or even a name, we had access.

Analytics8 was one of just a few organizations selected to participate in the private preview, giving us months to work directly with product and R&D teams — and to test, push, and learn in ways most companies won’t have the chance to.

We sat down with our CTO, Patrick Vinton, to talk about what we did with that early access, how it changed the way we think about AI, and why that experience puts us in a better position to help our clients make the most of this moment.

1. What is Agent Bricks, and what value does it bring to businesses?

Agent Bricks is Databricks’ new suite of AI solutions that makes it possible to build intelligent assistants on top of your enterprise data — both structured and unstructured — without having to build everything from scratch.

The easiest way to think about it? It’s like ChatGPT — but with secure access to your internal content. That includes policies, presentations, documentation, white papers, and more. You point Agent Bricks at your data lakehouse, and it starts returning results based on what your business knows, not just what the internet knows.

Historically, setting something like this up required a lot of behind-the-scenes plumbing: vector databases, embedding models, chunking strategies, front-end tools, role-based access layers, feedback loops, countless optimizations, and more. Agent Bricks abstracts all of that. You don’t have to build a chatbot UI. You don’t have to manage the back-end infrastructure. You just connect it to your data and go.

That’s the biggest shift: it moves AI implementation from something that takes months to something you can stand up in hours.

And the speed doesn’t come at the expense of governance. Because Agent Bricks sits within the Databricks lakehouse environment and is governed by Unity Catalog, you get the same enterprise-grade access controls, auditability, and security you’d expect from the rest of your data estate.

From our perspective, the biggest value is this: we no longer have to spend time building infrastructure — we can focus on helping clients with data quality, building data pipelines, and ultimately, connecting data to outcomes. That means aligning AI use cases to business goals, shaping a governance strategy, and preparing teams for adoption, instead of getting stuck in the technical setup.

2. Why was Analytics8 selected for early access?

Databricks product management knew we could bring a broad range of real-world use cases — and meaningful feedback — to the table.

Through conversations with solution architects, engineers, and product leaders, it was clear they wanted early input from partners who weren’t just experimenting but who had active, complex needs for internal and client-facing AI use cases. We had both. We brought everything from HR assistants to complex research bots built on top of multimodal documentation.

That variety made our feedback valuable, and the partnership mutually beneficial. They got insight into how the tools were performing across use cases, and we got direct visibility into the roadmap and the chance to influence what would launch (and what wouldn’t) in the initial release.

3. How did we use the opportunity, and what did we learn?

Being part of the private preview wasn’t just about testing — it gave us space to think critically and challenge long-held assumptions about how unstructured data fits into a modern AI strategy.

Most companies treat unstructured data as something that lives outside the analytics stack — in SharePoint folders, Google Drives, or scattered file shares. And while data lakehouses have always technically supported unstructured data, that support has rarely translated into actual lakehouse use cases.

Working with Agent Bricks forced us — in the best possible way — to confront that gap. If unstructured data is now powering AI assistants, it can’t live in the shadows anymore. It must be governed. It must be queryable. It must live in your lakehouse with the same access controls, lineage, and metadata that you’d apply to structured sources.

That shift completely changed how we approach data readiness and governance. We rethought how data pipelines should work. We built accelerators to help our clients ingest and catalog unstructured content quickly and securely. And we developed strong points of view on where unstructured data belongs in the broader architecture.

The other big unlock was time. Because we weren’t racing to catch up after a public launch, we got to move deliberately. We worked directly with Databricks’ R&D and product teams, helped influence the roadmap, and had the opportunity to think, not just react. That gave us clarity on how to approach Agent Bricks strategically, not just tactically.

The result? We’re not just familiar with the tool — we’re already helping clients make architectural decisions, define use cases, and avoid common mistakes. We didn’t just use the opportunity. We built on it.

4. How does that experience help us guide clients through their AI strategy — even if they’re just getting started?

We’ve already done the thinking, the testing, and the building, so clients don’t have to start from zero.

Because we were part of the preview, we’ve had time to refine our point of view on where these tools fit in an overall AI strategy. And we’ve started building assets and accelerators to make implementation faster and more repeatable. We’re not guessing at what works — we’re already applying what we’ve learned.

More importantly, we’ve seen where the real work still lies. The backend is largely taken care of now. What clients need help with is identifying the right use cases, getting their data in shape, and rethinking how AI fits into business workflows. That’s where we’re focused.

5. Most folks are just now learning about Agent Bricks. What’s something important they’ll miss if they don’t partner with someone who’s already been in the weeds?

The complexity under the hood — and what it takes to operationalize this.

When we first got access, there was no user interface. We were deploying from the command line, running scripts, and debugging raw outputs. That gave us a deep understanding of how the system works — and where the edge cases live.

That kind of exposure helps us move faster and troubleshoot better. It also helps us advise clients with confidence. Tools like this are powerful, but they’re not magic. You still need to understand what’s happening under the surface — or work with a partner who does.

6. What are you most excited about on the roadmap?

The ability to unify access to structured and unstructured knowledge through supervisor agents.

The individual services of Agent Bricks — like Knowledge Assistant or AI/BI Genie — are powerful on their own. But what’s even more exciting is what happens when you layer an agent on top that can decide which one to use — or when to use both.

We’re getting closer to that vision where users don’t have to know whether their question touches structured or unstructured data. They just ask, and agents figure out the best path. That’s what we’ve been chasing in analytics for a very long time.

7. Can you talk about our own product offering — what it does, and who it’s for?

We built an accelerator that creates a data pipeline for unstructured data coming from data sources like SharePoint and Google Drive to your lakehouse in a governed, production-ready way.

This wasn’t built just for Agent Bricks, but it’s become a critical piece in helping clients use it effectively. Most organizations have valuable content stuck in SharePoint or Google Drive — but none of it is available to their analytics stack.

Our accelerator bridges that gap. It moves files from those systems into Databricks’ lakehouse and applies governance through Unity Catalog. That’s what makes it usable — and safe — for AI. We’ve seen that it shortens implementation time and removes a major roadblock for clients who are ready to start but don’t have a clean way to access their content.