Data & AI on Databricks · Agentic AI on Agent Bricks

Build your agents where your data already lives — and where it's already governed.

Production AI agents on Databricks Agent Bricks — any model, any framework, grounded in your Unity Catalog data and governed end to end. The same custom agents you'd build on a separate stack, delivered inside the platform you already own — so governance reaches the agent, not just the tables.

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Databricks Partner
Bolted-on stack — glue code & a governance gap
vector storesecrets / IAMeval harnesslogging
The agentplanner · retriever · executor
⚠ governance gap — every hard question lands here
Governed data — out of reach
One boundary — nothing bolted on, no gap
Unity Catalog · one governance model
ModelDataTools
The agentplanner · retriever · executor
Same agent. Now it's governed.
Why this matters

The agent loop is the easy 1%. Governing it is the other 99%.

Standing up an agent that reasons and calls a tool is a weekend now. The hard part is everything around it — where its data came from, who's allowed to see it, whether you can prove it only touched what it was permitted to, how it's evaluated before it ships, and how it's watched once it's live. Build the agent as a separate stack and it lives outside the governance your data already has. That gap is where AI projects stall in review.

What a buyer's review actually asks about
~1%
The agent loop
Model reasons, picks a tool, takes a step. The part everyone demos.
→
99%
The hidden 99%
DeploymentSecurityEvaluationMonitoringMemoryCostSharing
Under all of it — governance: lineage · permissions · audit

Build the agent on a separate stack and it lives above that foundation, not inside it.

What we build

Custom agents on Agent Bricks — built with you, governed from the first call.

The same multi-agent systems we build anywhere — planner, retriever, executor — delivered natively on Agent Bricks, so they inherit your data, your permissions and your audit trail from day one.

01

Multi-agent systems, native

Planner, retriever and executor agents orchestrated on Agent Bricks — using Databricks' Omnigent meta-harness or your framework of choice, not a bespoke runtime we have to keep alive.

02

Grounded in your governed data

Agents reason over your Unity Catalog tables, documents and volumes, and reach external systems — Slack, Jira, Salesforce, SharePoint — through MCP, under the same permissions.

03

Evaluated, then shipped

Auto-generated benchmarks and LLM judges gate quality before launch; deployed serverless on Databricks Apps with SSO. A production agent, not a demo that never ships.

04

Built with Claude, our way

As a member of the Claude Partner Network and a Databricks partner, we build with Claude on Agent Bricks by default — frontier reasoning inside your governed boundary. Swap the model whenever you like.

How it runs

From prototype to production — without a second stack to secure.

Five stages, all inside one governed boundary. Nothing leaves the platform; nothing gets stitched on afterwards.

Unity Catalog · one governance model
→→→→
⟲ then back to Build for the next one
01 · BUILD

Build

Declarative or code-first. Describe the task and let Agent Bricks assemble and optimise it, or build in LangGraph or CrewAI with full control. Either way it's one platform, not glue code.

02 · GROUND

Ground

Connect the agent to your Unity Catalog data and MCP tools. Genie's semantic layer means it reasons over what your business means — not raw column names.

03 · EVALUATE

Evaluate

Agent Bricks generates task-aware benchmarks and LLM judges. Quality is measured and gated before anyone sees the agent — no “vibe check.”

04 · DEPLOY

Deploy

Serverless on Databricks Apps, with SSO and autoscaling, and a human in the loop wherever an action carries consequences.

05 · WATCH

Watch & improve

Traces, monitoring and PII alerts run through the platform; the agent improves from real feedback. Every step logged, permissioned and auditable — then back to Build.

One governed platform

Choice, Context, Control — the reason it holds up in review.

Agent Bricks is Databricks' agent platform. What makes it matter for a regulated buyer isn't the agent loop — it's that the model, the data, the tools and the controls sit in one place, under one governance model.

Unity Catalog · governed boundary
User→Agent→Model · frameworkData · tools · memory→Output
audit trail · identity: acts as the user
Same platform — swap the model, the framework stays governed
Model
Framework
GovernanceEvaluationServingAudit
These four never move. Swapping the model is a one-line change.
Why on Agent Bricks

Same agent. Delivered where it's governed.

You could build the same agent on a separate stack. Here's what that actually costs — and what disappears when it runs on Agent Bricks.

 
On a separate stack
On Agent Bricks
Data access
Wire up connectors, re-implement permissions
Inherited from Unity Catalog
Identity & secrets
A separate IAM and secrets store to secure
On-behalf-of identity, end to end
Retrieval & memory
Another system to run and govern
Native, governed, managed
Evaluation
Build the harness and the judges yourself
Auto-generated benchmarks and LLM judges
Guardrails
Bolted on per app
AI Gateway, on every model call
Logging & audit
Stitched together after the fact
Traces and lineage, built in
Switching models
Re-architecture
A one-line change
Time to production
Weeks of infrastructure work
Hours to days, on infrastructure you own

The agent is the same. The 99% around it is the difference — and on Agent Bricks it's already there. This is the same capability as our Agentic AI Development work, delivered natively inside the platform you already govern.

Proof, in production

Governed agents, on a platform enterprises already trust.

1 quadrillion+
tokens a year, across Agent Bricks production deployments
100,000+
agents built on Agent Bricks, Databricks reports

Agent Bricks is not experimental. It runs over a quadrillion tokens a year across production deployments, and Databricks reports more than a hundred thousand agents built on it. We build on it as a Databricks partner and a member of the Claude Partner Network — bringing the agentic engineering and the governance layer, while your team brings the domain.

Platform figures reported by Databricks (Data + AI Summit, June 2026) — the platform's track record, not our client references.

Who we build for

Built for teams standardising on Databricks — and the ones who have to prove their AI is safe.

Already on Databricks

Unity Catalog teams

You've done the hard governance work on your data. Agents on Agent Bricks activate on the platform you already run — no parallel stack, no second security review.

Regulated SaaS

FinTech · HealthTech · InsurTech

Where an agent's every action has to be explainable and auditable, native governance isn't a nice-to-have. It's how you get through procurement and the EU AI Act.

Going agent-native

Enterprises moving past pilots

Rolling out many agents to production and wanting one control plane — not a sprawl of one-off stacks each with its own security story.

Data doesn't leave

Residency-sensitive teams

Agents reason over your data inside your own governed environment — a straight answer to the residency and control questions European reviews start with.

Built for trust

Governance that reaches the agent — from a partner certified to build it.

AI Governance is the method — the phased lifecycle, the EU AI Act, the evidence packs. The Governed Data Platform is the control plane that enforces it. Agent Bricks is where the agent runs under both. We build the whole line — and we've shipped software for 13 years.

13+
Years shipping
200+
Clients served
ISO 27001
Certified
Databricks
Partner
Claude
Partner Network
Common questions

What a technical buyer asks before booking.

How is this different from us building agents ourselves in LangGraph?

It isn't a different agent — you can build in LangGraph, and we do. On Agent Bricks that agent is governed and production-ready from day one, instead of a separate stack you then have to secure, evaluate and audit yourself.

Are we locked into one model or one framework?

No. Any frontier or open model, any framework, and Databricks' Omnigent meta-harness on top — switch with a one-line change. The platform around the agent doesn't move when the model does.

Does it work with the Databricks and Unity Catalog we already run?

Yes — that's the point. Agent Bricks activates on the platform you already govern. There's no new environment to stand up and no second review of where your data sits.

How is it governed, and is it EU-AI-Act ready?

Unity Catalog governs the agent and everything it touches; identity is enforced end to end, with lineage from every output back to source. That's the evidence an EU AI Act or enterprise review asks for.

What if not all our data is in Databricks?

Agents reach external systems — Slack, Jira, Salesforce, SharePoint and more — through MCP, under the same permissions. And if the data foundation needs work first, that's our Modern Data Engineering practice.

How do we start?

One high-value task, one governed agent, proven in production — then expand to more agents and more of the workflow. Land small, expand with proof.

Let's build

Put an agent to work where your data already lives.

Book a 30-minute call. We'll find the highest-value first agent — governed from day one, running on Agent Bricks — and the path to expand from there.

Book a discovery call →Talk AI governance readiness
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