Agent tutorials and guides

Choose a tutorial to learn with a sample project, or follow a guide to apply an agent workflow to your own codebase in VS Code.

New to agents

Complete your first agent task

Recommended starting point. Build and validate a small web app, then review the changes.

Sample project. No runtime or build tools required.

Build an app step by step

Build a portfolio page while learning agent, editor, browser, and source control workflows.

Sample project. Requires Git.

Already use agents? Jump to a task:

Explore a codebase: trace a behavior without editing files and verify explanations against source references. Use your own repository.
Add a feature: research, plan, implement, and verify a bounded change. Use your own repository with a working development environment and tests.
Fix an API bug: reproduce a pagination bug, add a regression test, and review the fix. Sample project. Requires Node.js 22 or later and Git.
Refactor safely: change code structure in reviewable steps while preserving behavior and checking existing callers. Use your own repository.
Work with Jupyter notebooks: create or edit a notebook, run cells, and review analysis results. Use your own data. Requires the Jupyter extension and a notebook kernel.

Add tests to existing code: generate tests for a function or module, run them, and review assertions, failures, and coverage. Use your own project.
Validate a web app with browser tools: build a calculator app and check it against observable acceptance criteria. Sample web app.
Set up test-driven development: create custom agents and instructions for a repeatable test-first workflow. Workflow setup, rather than a one-off testing task.

Adapt agents to your project: share project instructions with your team, then add skills or specialized agents where needed. Use your own repository.
Set up a context engineering workflow: curate project context and connect planning to implementation with instructions, custom agents, and prompt files. Workflow setup.
Run independent tasks in parallel: work in separate Git worktrees, review each result, then integrate and retest the changes. Use your own Git repository after completing a single agent task.

Get an agent back on track: choose whether to steer a request, recover unwanted changes, or reset conversation context.
Reduce AI credit usage: compare model costs and results, focus context, and scope tools to reduce unnecessary usage.
Find prompt examples: adapt prompts for exploring code, building features, debugging, and testing.

How agents work: understand the agent loop and the role of models, context, and tools.
Best practices for using AI: scope tasks, provide relevant context, and verify results.
Troubleshoot AI features: diagnose setup, sign-in, connection, and other technical issues.

9/30/2026