How to Build AI Agents with Databricks in 2026: A Complete Guide

AI agents are moving beyond simple chatbots. In 2026, enterprises are using AI agents to retrieve information, reason over business data, call tools, execute workflows, and support employees and customers with increasingly autonomous systems.

For organizations already using Databricks, the platform provides a strong foundation for building these applications because data, AI models, retrieval, evaluation, governance, observability, and deployment can be connected within the same environment.

This guide explains how to build AI agents with Databricks in 2026, including the architecture, development process, RAG implementation, AI Search, Unity Catalog governance, MLflow evaluation, deployment, and production monitoring.

What Are AI Agents?

An AI agent is a software system that uses an AI model, tools, data, instructions, and sometimes memory to accomplish a goal.

Unlike a traditional chatbot that simply generates an answer, an AI agent can decide which action or tool it needs to use to complete a task.

A typical AI agent workflow looks like this:

User request → AI model → reasoning → tool selection → data retrieval or action → response

For example, an employee could ask:

“What was our sales performance in the western region last quarter, and which products contributed most to the decline?”

An AI agent could interpret the question, query governed business data, analyze the results, and generate an explanation instead of simply retrieving a predefined answer.

This ability to combine LLMs, tools, enterprise data, retrieval, and workflows is what makes AI agents particularly useful for enterprise applications.

Why Build AI Agents with Databricks in 2026?

Building an AI agent requires much more than selecting an LLM.

A production-ready agent needs:

• Access to trusted enterprise data
• Retrieval capabilities
• Tool calling
• Security and governance
• Evaluation
• Tracing and observability
• Scalable deployment
• Cost controls
• Monitoring

Databricks provides capabilities across these areas. Its current agent platform supports building, evaluating, and deploying agents ranging from simple LLM applications to tool-calling and multi-agent systems.

Databricks also supports agents built using frameworks such as LangGraph, LangChain, OpenAI Agents SDK, and LlamaIndex, giving development teams flexibility in choosing their preferred agent framework.

For enterprises, the major advantage is that AI applications can remain closely connected to governed business data rather than creating disconnected AI infrastructure.

Databricks AI Agent Architecture

A typical enterprise AI agent architecture on Databricks can contain the following layers:

User Interface

↓

Databricks App or Application

↓

AI Agent / Agent Framework

↓

LLM + Tools + Retrieval

↓

Databricks AI Search + Enterprise Data + APIs

↓

Unity Catalog Governance

↓

MLflow Tracing + Evaluation + Monitoring

The exact architecture depends on the use case.

A simple question-answering application may only require an LLM and AI Search. A sophisticated enterprise agent may require multiple tools, APIs, structured data, unstructured documents, memory, and multiple specialized agents.

Step 1: Define the AI Agent Use Case

Before writing code, define exactly what the agent should accomplish.

For example:

• Customer support agent
• Data analyst agent
• HR assistant
• Financial research agent
• IT support agent
• Sales intelligence agent
• Document analysis agent
• Healthcare information assistant
• Supply chain assistant

Avoid creating an agent that tries to do everything.

Instead, define:

1. What questions can the agent answer?
2. What data can it access?
3. Which tools can it use?
4. What actions can it perform?
5. What information must it refuse to provide?
6. What happens when it does not know the answer?

A well-defined boundary makes the agent easier to evaluate, secure, and maintain.

Step 2: Connect Enterprise Data

The quality of an AI agent depends heavily on the quality of the information available to it.

Enterprise information may exist in:

• Delta tables
• Documents
• PDFs
• Knowledge bases
• Databases
• Business applications
• APIs
• Data warehouses
• Internal systems

Databricks provides Unity Catalog to help govern data, AI assets, functions, models, and related resources.

This is particularly important for enterprise AI agents because an agent should not automatically have access to every piece of company information.

Access should follow the organization’s existing security policies.

Step 3: Add Retrieval with Databricks AI Search

Many enterprise AI agents need access to internal documents and unstructured information.

This is where RAG, or Retrieval-Augmented Generation, becomes useful.

Databricks AI Search, formerly known as Databricks Vector Search, provides managed search capabilities for generative AI applications. It supports similarity search, hybrid keyword and semantic search, filtering, reranking, and access controls.

A simplified RAG workflow looks like this:

User question

↓

Create query representation

↓

Search relevant enterprise documents

↓

Retrieve relevant context

↓

Send context + question to LLM

↓

Generate grounded response

AI Search can use indexes created from Delta tables and can keep indexes synchronized with underlying data in supported configurations.

Why RAG Matters for Databricks AI Agents

An LLM’s general training data does not automatically contain your organization’s latest information.

RAG allows the agent to retrieve relevant company information at query time.

For example, an internal HR agent could retrieve:

• Company policies
• Leave policies
• Employee handbooks
• Benefits documentation
• Internal procedures

The LLM can then use that retrieved context to formulate its response.

Step 4: Choose Your AI Agent Framework

Databricks supports multiple approaches to AI agent development.

You can use:

• Databricks-native agent capabilities
• OpenAI Agents SDK
• LangGraph
• LangChain
• LlamaIndex
• Custom Python implementations

The best choice depends on the complexity of your application.

For example, a simple agent may only need tool calling and retrieval.

A complex workflow involving state, multiple agents, conditional execution, and human approval may benefit from a more advanced agent framework.

Current Databricks documentation specifically demonstrates agents built with OpenAI Agents SDK and explains that agents created with different frameworks can integrate with Databricks through the ResponsesAgent interface.

Step 5: Give Your Agent Tools

Tools are what allow an AI agent to move beyond generating text.

A tool could allow the agent to:

• Query SQL data
• Search documents
• Call an API
• Retrieve customer information
• Calculate metrics
• Execute a business function
• Search a knowledge base
• Create a ticket
• Retrieve inventory information

For example, a sales agent could have tools such as:

get_customer_data()

get_sales_data()

search_product_catalog()

create_sales_report()

The LLM determines which tool is appropriate based on the user’s request.

Databricks supports connecting agents to tools that can work with structured and unstructured data as well as external services. MCP, or Model Context Protocol, can also provide a standardized way for agents to connect to tools and data sources.

Step 6: Build the Agent Logic

The basic logic of a tool-calling AI agent can be represented as:

Receive user request

       ↓

Understand intent

       ↓

Determine required information

       ↓

Select tool or retrieval source

       ↓

Execute tool

       ↓

Analyze result

       ↓

Generate final response

For a simple agent, this may involve a single tool.

For a more advanced system, the agent may need to perform multiple actions before returning an answer.

For example:

User

  ↓

Sales Agent

  ↓

Customer Data Tool

  ↓

Sales Data Tool

  ↓

Analytics Tool

  ↓

LLM

  ↓

Business Recommendation

This is where AI agent development becomes different from traditional chatbot development.

The application is no longer simply:

Prompt → Response

Instead, it becomes:

Goal → Reasoning → Tools → Data → Actions → Result

Step 7: Add Memory When Necessary

Some AI agents need memory.

For example, a customer service agent may need to remember the conversation history.

A research assistant may need to maintain information collected during a session.

However, memory should not be added simply because it is technically possible.

Ask whether the application actually requires:

• Conversation history
• User preferences
• Session state
• Long-term memory
• Intermediate workflow state

Databricks provides options for managing persistent application state, depending on the architecture and requirements.

For enterprise applications, memory should also be governed because conversation history can contain sensitive business information.

Step 8: Evaluate Your AI Agent

One of the biggest mistakes teams make is testing an AI agent only by manually asking a few questions.

A production AI agent needs systematic evaluation.

You should measure factors such as:

• Answer quality
• Correctness
• Relevance
• Retrieval quality
• Tool selection
• Tool execution
• Hallucination
• Latency
• Cost
• Safety
• User satisfaction

Databricks and MLflow provide tools for evaluating and monitoring GenAI applications throughout the development lifecycle. MLflow Tracing can capture agent behavior, while evaluation tools can use built-in or custom judges and scorers.

Build an Evaluation Dataset

Create a dataset containing representative questions and expected behaviors.

For example:

User Question	Expected Behavior
What is our leave policy?	Retrieve HR policy
What were Q2 sales?	Query sales data
Show customer X revenue	Query customer database
Give me confidential employee data	Refuse
What is our return policy?	Retrieve policy document

Databricks also provides capabilities for synthetically generating evaluation datasets for agents that use document retrieval, helping teams increase test coverage.

Step 9: Add MLflow Tracing and Observability

AI agents can be difficult to debug because a single response may involve several internal operations.

For example:

User request

    ↓

LLM

    ↓

Tool selection

    ↓

AI Search

    ↓

Retrieved documents

    ↓

Second LLM call

    ↓

Final answer

If the final response is incorrect, you need to know where the problem occurred.

Was the model wrong?

Was the wrong tool selected?

Did retrieval return poor documents?

Was the prompt unclear?

Did the API return incorrect data?

MLflow Tracing helps developers inspect these operations and understand agent behavior. Databricks recommends tracing and evaluation across development and production so teams can continuously improve GenAI applications.

Step 10: Secure the AI Agent with Unity Catalog

Enterprise AI agents require strong governance.

Unity Catalog can be used to manage permissions around data and AI assets.

For example, an agent might be allowed to:

• Read a sales table
• Query a customer dataset
• Search approved documents

But it should not automatically have permission to:

• Delete production data
• Access payroll information
• Read restricted customer records
• Execute arbitrary SQL
• Call unauthorized external APIs

The principle should be:

Give the agent the minimum permissions required to complete its job.

Databricks’ current AI governance architecture extends Unity Catalog governance across models, agents, MCP services, tools, and related AI assets.

Step 11: Use Unity AI Gateway for Enterprise AI Governance

As organizations deploy more AI agents, managing model and agent traffic becomes increasingly important.

The current Unity AI Gateway provides centralized controls for AI services, including model, agent, and MCP interactions.

Capabilities include:

• Access control
• Rate limiting
• Fallbacks
• Budget management
• Usage tracking
• Cost monitoring
• Request and response logging
• Security policies
• Guardrails

Databricks describes Unity AI Gateway as a governance layer for enterprise AI that extends controls beyond data assets to AI traffic and runtime interactions.

This becomes particularly valuable when an organization has multiple AI agents and multiple model providers.

Step 12: Deploy the AI Agent

Once the agent passes evaluation, it can be deployed for users.

Databricks supports deployment options including Databricks Apps and agent-serving capabilities.

The current Databricks agent tutorial demonstrates an architecture using an MLflow AgentServer, an agent framework, MCP connectivity, and a conversational application interface.

Databricks Apps can also be used to create interactive interfaces around agents.

A simplified production architecture could look like:

                   Users

                      |

                      v

              Databricks App

                      |

                      v

                  AI Agent

                 /   |   \

                /    |    \

               v     v     v

             LLM   Tools  AI Search

                     |       |

                     v       v

                  Business  Documents

                    Data

                     |

                     v

               Unity Catalog

                     |

                     v

              MLflow Monitoring

Step 13: Monitor the Agent in Production

Deployment is not the end of AI agent development.

Once users start interacting with the agent, monitor:

Quality

• Correct answers
• Hallucination rate
• Retrieval quality
• Tool accuracy

Performance

• Response latency
• Throughput
• Error rate
• Availability

Cost

• Token consumption
• Model usage
• Tool execution costs
• Infrastructure costs

Security

• Unauthorized access
• Prompt injection
• Sensitive information exposure
• Unsafe tool execution

Production traces can also become valuable evaluation data for improving future versions of the agent.

Databricks AI Agents vs Traditional Chatbots

Traditional chatbots usually follow predefined flows.

AI agents are more flexible because they can determine which information or tools are required for a particular task.

Traditional Chatbot	AI Agent
Predefined responses	Dynamic responses
Fixed workflows	Adaptive workflows
Limited tool usage	Multiple tools
Often rule-based	LLM-driven
Limited reasoning	Multi-step reasoning
Static knowledge	Retrieval and live data
Difficult to scale to complex tasks	Designed for complex tasks

This does not mean every chatbot should become an AI agent.

For simple FAQs, a traditional application may still be the better solution.

AI agents are most valuable when the problem requires reasoning, data retrieval, tool usage, or multi-step execution.

Databricks AI Agent Use Cases

Organizations can use Databricks AI agents across many industries.

1. Data Analyst Agent

A data analyst agent can interpret natural-language questions and retrieve business metrics from governed data.

Example:

“Compare revenue growth between North America and Europe over the last four quarters.”

The agent could query the appropriate datasets and explain the results.

2. Customer Support Agent

A customer support agent can combine:

• Customer records
• Product documentation
• Support history
• Knowledge bases
• Business APIs

It can then provide context-aware responses.

3. Enterprise Knowledge Agent

An enterprise knowledge agent can search:

• PDFs
• Policies
• Internal documentation
• Procedures
• Product information

Databricks AI Search can provide the retrieval layer for these RAG applications.

4. Financial Analysis Agent

A financial agent could retrieve financial data, calculate metrics, compare periods, and generate analytical summaries.

5. IT Support Agent

An IT agent could search technical documentation, investigate system information, and interact with approved service-management tools.

Databricks AI Agents and Multi-Agent Systems

Some complex enterprise applications may require more than one agent.

Instead of creating a single agent responsible for everything, organizations can create specialized agents.

For example:

               Supervisor Agent

                       |

       +—————+—————+

       |               |               |

       v               v               v

  Data Agent      Research Agent   Support Agent

       |               |               |

       v               v               v

  SQL Tools        AI Search       APIs

A supervisor can determine which specialized agent should handle a particular task.

Databricks documentation currently includes support for multi-agent architectures alongside custom agents, Knowledge Assistant, Supervisor Agent, MCP servers, and other agent development capabilities.

Best Practices for Building AI Agents on Databricks

Start with a narrow use case

Do not build an agent that tries to solve every enterprise problem.

Start with one measurable business workflow.

Ground responses in trusted data

Use retrieval and governed enterprise data when the agent needs company-specific information.

Limit tool permissions

Only expose the tools and data required by the agent.

Evaluate before deployment

Create a representative evaluation dataset before releasing the agent to users.

Monitor continuously

Track quality, latency, cost, and security after deployment.

Use human feedback

Subject matter experts can identify failure modes that automated evaluation may miss.

Design for failure

The agent should know when it does not have enough information.

A good agent should be able to say:

“I don’t have enough information to answer that reliably.”

instead of inventing an answer.

Common Mistakes When Building Databricks AI Agents

1. Starting with the model instead of the problem

Choosing an LLM should not be the first step.

Start with the business problem.

2. Giving the agent unrestricted access

More access does not necessarily mean a better agent.

It increases security risk.

3. Skipping evaluation

A successful demo does not prove production readiness.

4. Ignoring retrieval quality

If the RAG system retrieves irrelevant information, even a powerful LLM can produce poor answers.

5. Not monitoring costs

Agent workflows can make multiple model and tool calls for a single user request.

6. Treating an AI agent like a normal application

AI systems are probabilistic.

Their behavior needs continuous evaluation and monitoring.

Databricks AI Agent Development Lifecycle

A reliable development lifecycle can be summarized as:

1. Define the use case

↓

2. Identify data and tools

↓

3. Select the agent framework

↓

4. Build the initial agent

↓

5. Add RAG / AI Search

↓

6. Add tools and integrations

↓

7. Apply Unity Catalog governance

↓

8. Evaluate with MLflow

↓

9. Deploy

↓

10. Monitor

↓

11. Improve

This iterative process is more effective than building the entire system first and testing it at the end.

What Makes Databricks a Strong Platform for Enterprise AI Agents?

The biggest advantage of Databricks is not simply that it can run an LLM.

The value comes from bringing together the components required to operate AI applications in an enterprise environment.

These include:

• Enterprise data
• AI models
• AI Search
• RAG
• Agent frameworks
• Tool integrations
• Unity Catalog
• MLflow
• AI governance
• Evaluation
• Monitoring
• Application deployment

Databricks’ current AI platform documentation positions these capabilities across the full agent lifecycle, from prototyping and development to evaluation, deployment, governance, and production monitoring.

Final Thoughts

Learning how to build AI agents with Databricks in 2026 is no longer just about connecting an LLM to a prompt.

Production AI agents require a complete architecture that combines models, enterprise data, retrieval, tools, security, evaluation, deployment, and monitoring.

Databricks provides a unified environment for building this type of application. With capabilities such as Mosaic AI Agent Framework, Databricks AI Search, Unity Catalog, MLflow, Unity AI Gateway, Databricks Apps, and MCP, development teams can move from an experimental AI agent to a governed production application.

The most effective approach is to start small, select a clear business use case, connect the agent to trusted data, restrict its tools and permissions, evaluate its behavior, and continuously monitor its performance.

For organizations already using Databricks, this makes the platform a practical foundation for developing enterprise AI agents that can reason over data, use tools, and execute real business workflows.

Frequently Asked Questions

What is a Databricks AI agent?

A Databricks AI agent is an AI-powered application built and deployed using Databricks capabilities that can use LLMs, enterprise data, retrieval systems, tools, and workflows to accomplish tasks.

How do I build an AI agent on Databricks?

Start by defining the use case, connecting governed data, selecting an agent framework, adding tools or RAG, implementing the agent logic, evaluating it with MLflow, applying governance, and deploying it through supported Databricks deployment options.

Can I use LangGraph with Databricks?

Yes. Databricks supports agents developed using frameworks including LangGraph, LangChain, OpenAI Agents SDK, and LlamaIndex.

Is Databricks Vector Search still available?

Databricks Vector Search has been renamed Databricks AI Search. Current documentation uses the AI Search name, while many existing tutorials and searches still refer to Databricks Vector Search.

Can Databricks AI agents use company documents?

Yes. AI agents can use retrieval systems such as Databricks AI Search to retrieve relevant information from enterprise documents and other data sources for RAG applications.

How do you evaluate AI agents on Databricks?

Databricks integrates agent evaluation and MLflow capabilities for measuring application quality, tracing agent behavior, collecting feedback, and monitoring production performance.

Can Databricks AI agents be deployed to production?

Yes. Databricks provides deployment options including Databricks Apps and agent serving capabilities, with MLflow-based tracing and evaluation supporting the production lifecycle.

What is the difference between RAG and an AI agent?

RAG primarily focuses on retrieving relevant information and providing it to an LLM. An AI agent can go further by deciding what actions to take, calling tools, retrieving information from different sources, and executing multi-step workflows.