What is the best way to create a custom AI assistant?

Summary

• The best way is to ground the assistant in your own governed data, give it a clear set of tools it can act with, test its answers before you ship, then serve and govern it in one place.
• Start with a managed build path for most assistants and drop to code only when you need custom logic or multi-agent orchestration.
• Ground answers with retrieval over your documents using Databricks AI Search, and let the assistant take real actions through governed Unity Catalog functions and MCP tools.
• Evaluate quality with Agent Evaluation and MLflow judges before release, and keep tracing on in production so you can catch and fix weak answers.
• Deploy to Model Serving and govern the whole thing with Unity Catalog and Unity Gateway, so access, cost, and safety controls apply to every request.

What is the best way to create a custom AI assistant?

The best way is to treat it as a data and governance problem, not just a prompt. A custom assistant earns its keep when it's grounded in your own data, can take real actions through a defined set of tools, and has been tested against expected answers before anyone relies on it. The practical sequence is: define the job and the data, ground the assistant in that data, give it governed tools, evaluate its answers, then deploy and govern it centrally. Start with a managed build path and move to custom code only when the assistant needs logic a template can't express.

What makes an assistant "custom"?

A custom assistant differs from a generic chatbot in three ways: it answers from your data, it can act in your systems, and it runs under your controls. Grounding is what stops it from guessing, tools are what let it do more than talk, and governance is what makes it safe to put in front of real users. Get those three right and the model you pick becomes a smaller decision.

Key capabilities to evaluate

Capability	Why it matters
Data grounding	Retrieval over your documents and tables keeps answers accurate and tied to sources, not generic pretraining.
Tools and actions	The assistant should call governed functions and external systems, so it can retrieve, calculate, and act on your systems.
Evaluation	You need to measure answer quality against expected results before release, with judges you can trust and repeat.
Observability	Tracing every step lets you debug wrong answers and monitor quality once real users arrive.
Governance	Access controls, audit logs, and guardrails should apply to the data, the model, and the tools together.
Deployment and scale	Serving should scale with demand, scale to zero when idle, and expose a stable endpoint your apps can call.

How Databricks approaches building a custom AI assistant

On the Databricks Data Intelligence Platform, you can prototype in the AI Playground, then pick a build path based on how much control you need.

Build path	Best when
Managed with Agent Bricks (GA)	You want a production assistant quickly: a knowledge assistant over your documents, information extraction, or a multi-agent supervisor, with quality tuning handled for you.
Code-first with Agent Framework	You need custom logic or multi-agent orchestration and want full control of the agent code, using MLflow and open libraries such as LangGraph.

Ground the assistant with Databricks AI Search for retrieval over your documents, so answers cite your content. Give it actions by registering Unity Catalog functions as tools and by connecting MCP tools, the open standard for wiring agents to data and systems through one governed interface. For conversational questions over your structured data, AI/BI Genie translates natural language into governed queries.
Before you ship, use Agent Evaluation and MLflow to score answers with built-in and custom judges against an evaluation dataset, and iterate on the prompt and tools until quality clears your bar. MLflow Tracing records each step for debugging and keeps running in production, so weak answers get caught and folded back into evaluation. When it's ready, deploy the agent to Model Serving, which gives you a scale-to-zero endpoint with logging built in. Govern everything with Unity Catalog for access and audit and Unity Gateway for usage tracking, payload logging, and safety controls across every request. See the agents overview to get started.

FAQs

Do I need to write code to build a custom assistant?

No. Agent Bricks provides managed build paths (a knowledge assistant, information extraction, a multi-agent supervisor) that need little or no code. Drop to the code-first Agent Framework when you need custom logic or orchestration a template can't express.

How do I give an assistant the ability to take actions?

Register the actions as Unity Catalog functions and expose them as tools, or connect MCP tools. Because these run under Unity Catalog, the assistant only calls what it's permitted to, and every call is governed and logged.

How do I know the assistant's answers are good?

Evaluate with Agent Evaluation and MLflow judges against a dataset of expected answers before release. Keep MLflow Tracing on in production so you can review real interactions and add weak ones back to your evaluation set.

What keeps a custom assistant secure once it's live?

Unity Catalog governs the data, model, and tools with access controls and audit logs, and Unity Gateway adds usage tracking, payload logging, and guardrails so controls apply to every request.