Name an agent’s parts
Identify the key components of an AI agent and how simple, agentic and multi-agent systems differ.






























AI Agent Fundamentals is a free Databricks course of four short videos, about 90 minutes in all, with a quiz that earns a badge. It covers an agent’s parts, how simple, agentic and multi-agent systems differ, enterprise use, industries and Agent Bricks. It is a badge for fundamentals, not a proctored certification, and Databricks lists no passing score, question count or expiry.
Identify the key components of an AI agent and how simple, agentic and multi-agent systems differ.
Explain how generative AI and large language models work in enterprise settings and link to agents.
Recognize how agents are applied across several major industries.
Describe what Databricks’ AI tools and Agent Bricks offer for building and deploying agents.
We design this course ourselves. Databricks issues the credential, and LUNARTECH is independent of Databricks.
An agent is more than a chat window. You learn its components, such as a model, instructions, tools and memory, and how simple, agentic and multi-agent systems differ in what they can handle.
You build A labeled diagram of an agent for a support desk, with each part named.
The language model is the engine of an agent, and how it behaves in a company matters. You see how models work in an enterprise setting and how they connect to the data and tools an agent relies on.
You build A note tracing one agent answer back to the model, data and tool that produced it.
Seeing agents at work makes the idea concrete. You look at real applications across several major industries, and learn to recognize the pattern of a task that an agent handles well.
You build A table of three industry examples with the task, tools and risk of each.
Building an agent takes tools for data, models and deployment. You learn what Databricks provides for these, and what Agent Bricks offers for building and deploying agents without starting from scratch.
You build A map linking each part of an agent to the Databricks tool that supports it.
You pull the ideas together by planning a small agent: its job, its tools, the data it needs and the limits it must respect. The plan shows where you would start and what could go wrong.
You build A one-page plan for a first agent with its tools, data, limits and first milestone.

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No single job owns it. Here’s where each role leans hardest, and where its work ends up.
Agents change who does the routine work. Knowing the types helps you scope it.
Components, Industries
Enterprise teams, Customer service, Financial services
Start from the parts of an agent. Then see what the platform already gives you.
Components, Models, Tooling
Software products, Customer service
Agents read the data you manage. Knowing how they use it shapes your pipelines.
Models, Tooling
Enterprise teams, Software products
You decide when one agent is enough and when to split the work. Knowing the parts helps you scope what you will build.
Components, Models, Tooling
Enterprise teams, Software products
An agent is a loop around a model, tools and some state. Tooling choices decide how easily you can test it.
Components, Tooling
Software products, Customer service
Industry examples show which tasks agents already handle well. You can tell a real agent from a scripted chatbot.
Industries, Components
Customer service, Enterprise teams
You find routine steps in a process that an agent could take. Real uses across industries give you patterns to borrow.
Industries
Financial services, Enterprise teams
Support is a common first agent, so you meet them early. Knowing how models connect helps you judge a vendor’s claims.
Industries, Models
Customer service, Enterprise teams
Agents change who does the routine work. Knowing the types helps you scope it.
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Updated November 2026.
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Learn what AI agents are in 90 minutes, free. Start with a free trial, or enroll in a plan and work through the course at your own pace. Databricks issues the credential.
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