Which AI agent software is beginner friendly?

Summary

• Beginner friendly depends on whether you write code. Databricks documents one path for each: Genie Agents for creating agents without writing agent code, and Agent Bricks for developers who write their own agent code.
• With Genie Agents, you click New, choose your data sources, and click Create. Genie Code launches during setup and suggests table descriptions and example queries.
• With Agent Bricks, a quickstart takes you from an empty directory to a deployed agent using the Agent Bricks CLI. Tracing is on by default.
• Agent Runtime lets you deploy code instead of provisioning servers.
• The Agent Bricks CLI and quickstart, Databricks Sandbox, and managed agent memory are Beta.

Which AI agent software is beginner friendly?

Beginner friendly depends on who is building. If you do not write code, look for software that lets you create an agent from your data in a guided interface. If you write code, look for a documented quickstart, templates for frameworks you already know, a way to test locally, and a deployment step that does not make you run servers. Databricks documents both paths: Genie Agents for creating agents without writing agent code, and Agent Bricks for developers who write their own agent code.

What makes agent software beginner friendly?

Capability	Why it matters
Guided creation	You can create an agent from your data without writing code
Quickstart	A documented path from an empty directory to a running agent
Templates	A working project for a framework you already know
Local testing	You can run the agent and check it before you deploy
Managed hosting	You deploy code instead of provisioning servers
Tracing	You can see each step the agent took and fix what went wrong
Lifecycle labels	You know which parts are Beta before you build on them

How does Databricks approach beginner-friendly agents?

If you do not write code, start with Genie Agents. A Genie Agent is a domain-specific natural-language chat interface where users ask questions of their data and get back SQL queries, results tables, and visualizations. To create one, click Genie Agents in the sidebar, click New, choose your data sources, and click Create. During setup, Genie Code launches automatically. It reads your data and suggests context such as descriptions of your tables and example queries. Before you share the agent, add example SQL queries, instructions, and knowledge store configurations to improve response accuracy. You can test the agent, run benchmarks, and review its performance, and you can attach Unity Catalog Volumes so users can ask questions about unstructured files alongside structured data. The data must be registered to Unity Catalog, and Genie Agents require a pro or serverless SQL warehouse. In the docs, data analysts curate each agent, and users then ask it questions in natural language.
If you write code, start with the Agent Bricks quickstart. Agent Bricks is the Databricks agent developer platform, built for developers who write code. The quickstart is in Beta. You need the Databricks CLI on your path and Python 3.10 or above with pip, then install the Agent Bricks CLI with pip install databricks-agentbricks. The quickstart has four steps:

1. Authenticate with databricks auth login and agentbricks login.
2. Scaffold a project with agentbricks init --framework langgraph my-agent.
3. Run the agent on your machine with agentbricks dev.
4. Deploy it with agentbricks deploy my-agent.

The --framework flag takes langgraph for LangGraph or openai for the OpenAI Agents SDK. If you already built an agent with either framework, the --existing flag moves it to the Agent Bricks CLI. You write the application logic (model, tools, and prompts), and the CLI handles running it locally and deploying it to Databricks infrastructure. The agent.toml file is the declarative source of truth for the Databricks-managed resources your agent depends on, such as tool bindings and memory, session, and tracing resources.
Hosting, debugging, and safe code execution. Agent Runtime runs your agent server on Databricks-hosted serverless compute, so you deploy code instead of provisioning servers. MLflow Tracing records the inputs, outputs, latency, token usage, and cost of every intermediate step, and tracing is on by default in the CLI. Agents run the code they write in Databricks Sandbox (Beta), an isolated environment. Managed agent memory (Beta) gives an agent durable memory across conversations, stored in Lakebase.

Which path fits you?

If you	Use	Starting point
Do not write code and want an agent over your tables and documents	Genie Agents	Genie Agents, then New, then choose data sources
Write code and want to deploy a custom agent	Agent Bricks	The Agent Bricks quickstart (Beta)
Are a business user who wants one chat interface across data assets, dashboards, and apps	Genie One	Ask questions of your data in Genie One

FAQs

Can I build an agent without writing code?

Yes. Databricks says to build agents without writing code, use Genie Agents. Agent Bricks is for developers who build custom agents in code.

Do I need to know a specific agent framework?

No. Agent Bricks works with any framework or harness. The CLI includes templates for LangGraph and the OpenAI Agents SDK.

Do I need to manage servers?

No. Agent Runtime runs your agent server on Databricks-hosted serverless compute, so you deploy code instead of provisioning servers.

What if my agent makes a mistake?

Use MLflow Tracing to see the inputs, outputs, latency, token usage, and cost of each step, then change the agent and redeploy.

Is everything generally available?

No. The Agent Bricks CLI and quickstart, Databricks Sandbox, and managed agent memory are each labeled Beta in the docs. Check the label on each component before you build on it.