Data + AI
Move AI beyond experimentation and into governed production environments.
Models and agents cannot compensate for fragmented, poorly governed, poorly understood enterprise data. We design ML and agentic systems on Unity Catalog, MLflow, Model Serving, Agent Bricks, and AI Search—with evaluation and monitoring as part of the architecture.
AI doesn't fix fragmented data. It exposes it.
Most stalled AI programs fail on context, permissions, and operational standards—not on model choice. An agent with access to the wrong table, or no lineage, is not an intelligence system. It is a liability.
Pilot islands
Notebooks and vector indexes live outside the catalog, identity model, and production account.
RAG on ungoverned files
Retrieval is pointed at document dumps nobody would trust in a dashboard.
No evaluation loop
Quality is judged by a demo. There is no trace, no monitor, and no owner after launch.
Outcomes
Governed access to enterprise data
Agents and models inherit Unity Catalog permissions and identity rather than bypassing them.
A production ML/AI path
MLflow, serving, evaluation, and monitoring are designed before the second prototype starts.
Architecture for agents
Retrieval, tools, and application boundaries are explicit. The agent is not allowed to invent access.
Capabilities
- MLflow
- Agent Bricks
- Model Serving
- AI Search
- Evaluation
- Model lifecycle management
- AI application architecture
- Enterprise RAG
- Agent architecture
- Production monitoring
- Governed access to enterprise data
Production AI is a data-platform problem with a model in the loop.
We start from the data products, permissions, and operational workflows the system must respect. Model serving and agent design come after those constraints are real.
01
Context and permission design
Which governed data, features, and documents the system is allowed to use—and as whom.
02
Lifecycle and serving
Training or retrieval architecture, MLflow tracking, evaluation, Model Serving, and application integration.
03
Operate
Tracing, quality monitors, cost controls, and the owners who can shut it off.
Common scenarios
Enterprise RAG
Answers grounded in governed organizational content rather than in a shared drive.
Predictive systems
Classical ML on trusted historical and real-time features, with a path to serving.
Agentic workflows
Agents that query lakehouse data, call tools, and must not exceed the user's authorization.
Why this approach
AI starts with data
If the organization would not put a number on an executive dashboard, it should not be in an agent prompt context either.
Identity is non-negotiable
Production agents should operate with governed identity. Permission bypass is not a feature.
Evaluation before rollout
MLflow and operational tracing exist to make quality visible. Demos are not a control system.
Questions
Do you build custom models or use foundation models?
Whichever the problem requires. Many enterprise systems combine governed retrieval, existing ML models, and hosted foundation models behind Model Serving. The architecture is driven by data sensitivity, latency, and evaluation—not by a preferred brand of model.
What is Agent Bricks in this context?
Agent Bricks is Databricks' enterprise surface for building, deploying, and governing agents on business data. We treat it as part of the platform architecture alongside Unity Catalog, MLflow, and Model Serving—not as a standalone chatbot project.
Can we start AI work before the lakehouse is finished?
You can start on a domain that already has trusted, governed data. Expanding AI across ungoverned sources is how programs lose executive confidence.
Related insights
AI & ML
How to Prepare Enterprise Data for AI Agents
Agents inherit whatever you give them: permissions, definitions, and quality. Preparing data for agents is lakehouse work, not prompt work.
10 min
Databricks Architecture
Databricks Architecture Best Practices for Enterprise Teams
A lakehouse becomes an operating layer only when environments, catalogs, workloads, and consumption are designed as one system—not as a growing pile of workspaces.
12 min
Start with the business case
Find the first data or AI opportunity worth proving.
We evaluate the business problem, systems, data, architecture, and economics behind it—then identify the smallest production engagement capable of proving whether the opportunity is real.
Business case first · Architecture-led · Production-focused