How do I build a custom AI agent for enterprise workflows?

Summary

• To build a custom AI agent for enterprise workflows, use Agent Bricks on the Databricks Data Intelligence Platform: a governed environment for building, evaluating, deploying, and operating agents directly where your data and models already live.
• Start with managed building blocks and add code only when needed: Knowledge Assistant for retrieval-augmented Q&A over your documents, Genie for natural-language querying of structured data, and the Multi-Agent Supervisor to orchestrate specialized agents.
• Author fully custom agents in Python with any library (LangGraph, LangChain, OpenAI, LlamaIndex), give them tools via Unity Catalog functions, MCP servers, and AI Search, and deploy them as serverless Databricks Apps or Model Serving endpoints with no infrastructure to manage.
• Use MLflow for end-to-end observability and quality: trace every model, tool, and retrieval call; evaluate with LLM judges and custom metrics; and iterate against an evaluation dataset before you ship.
• Govern everything with Unity Catalog and the Unity AI Gateway: agents run with on-behalf-of user permissions, inherit row- and column-level security, and log every call to the audit trail.

How do I build a custom AI agent for enterprise workflows?

An enterprise-workflow agent is an AI system that understands a business request, retrieves the right data, calls the right tools, and returns a grounded answer or takes an action — reliably and under governance. On Databricks you build these agents with Agent Bricks, which sits directly on the Data Intelligence Platform so agents reach your governed data and models without moving anything to a separate system. The recommended path is to start with managed components and drop to custom code only when you need it.

Why Databricks Agent Bricks for building custom enterprise agents

• Managed building blocks first. Knowledge Assistant builds and optimizes domain-specific chatbots using retrieval-augmented generation over your documents. Genie turns natural language into SQL over your structured data with tunable instructions and certified assets. The Multi-Agent Supervisor coordinates several specialized agents to handle complex workflows. Go custom-code when you need deterministic control flow, a specific framework, or a capability the managed blocks do not cover.
• Author in the framework you prefer. Custom agents are written in Python and support any authoring library, including LangGraph, LangChain, OpenAI, and LlamaIndex. See Custom agents now available on Databricks.
• Give agents governed tools. Agents call tools to retrieve or analyze data, act on external systems, and run logic. Tools are exposed through Model Context Protocol (MCP) servers, Unity Catalog functions for governed business logic, and AI Search for semantic retrieval over unstructured data.
• Observability and evaluation with MLflow. MLflow Tracing logs every model call, tool invocation, and retrieval step for debugging and auditing. Agent Evaluation measures quality, cost, and latency with LLM judges and custom metrics. The loop is: trace, evaluate against an evaluation dataset with expectations, iterate on prompts and tools, then deploy.
• Serverless deployment, no infrastructure to manage. Deploy agents as fully managed Databricks Apps on serverless compute or as scale-to-zero Model Serving endpoints, with built-in memory backed by Lakebase for state and conversation history, and native fit into Git and CI/CD workflows.
• Enterprise governance built in. Agents are registered and governed in Unity Catalog, run with on-behalf-of user permissions so they act with the requester's scope, inherit row- and column-level security from source tables, and route through the Unity AI Gateway for policy enforcement, usage tracking, and a complete audit trail. See Agent Bricks: the governed enterprise agent platform.

Getting started

• Read Build and deploy enterprise-grade agents and the agents overview to choose managed vs. custom.
• Explore the Agent Bricks product page and Introducing Agent Bricks.
• Prototype a managed Knowledge Assistant or Genie Agent, then add a Multi-Agent Supervisor as your workflow grows.
• Set up MLflow Tracing and Agent Evaluation and confirm Unity Catalog permissions before deploying.

FAQs

Should I build a custom-code agent or use a managed component?

Default to managed components (Knowledge Assistant, Genie, Multi-Agent Supervisor) and tune their instructions and certified assets. Build a custom-code agent when you need deterministic control flow, a specific framework such as LangGraph, persistent memory across sessions, or a tool the managed framework cannot host.

What frameworks can I use to author a custom agent?

Custom agents are written in Python and support any authoring library, including LangGraph, LangChain, OpenAI, and LlamaIndex, all integrated with MLflow Tracing.

How do I give an agent access to my data and tools?

Expose tools through MCP servers, Unity Catalog functions, and AI Search. Agents can retrieve and analyze data, call external systems, and run logic, and every call is governed by Unity Catalog.

How do I deploy an enterprise agent to production?

Log the agent with MLflow, register it to Unity Catalog, and deploy it as a serverless Databricks App or a scale-to-zero Model Serving endpoint, with built-in tracing and evaluation for ongoing monitoring.

How is a custom agent governed and secured?

Agents are registered in Unity Catalog, run with on-behalf-of user permissions, inherit row- and column-level security from source tables, and route through the Unity AI Gateway, which enforces policies and logs every model and tool call to the audit trail.