AI Agents for Beginners
A free 18-lesson course that teaches you everything you need to start building AI Agents.
- Source repo
- microsoft/ai-agents-for-beginners
- Stars
- ★ 77k
- Last updated
- 1d ago
- License
- MIT
- Primary language
- Jupyter Notebook
- FA score
- 46/100 · Major gaps
At a glance
- How it runs
- Works with
- Platform-specific
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- Beginners who want a structured, comprehensive path to learn AI agent development from scratch.
- Not a fit if
- Developers committed to non-Microsoft frameworks who want to avoid vendor lock-in
- Teams wanting a deployable product; this is a tutorial, not an application
- Source review
- 46/100 · Major gaps 8 safety controls not found
What does this agent do, and when should you use it?
Microsoft's AI Agents for Beginners is a comprehensive, free course designed to teach the fundamentals of building AI agents. It consists of 18 lessons covering topics such as intro to AI agents, exploring agentic frameworks, design patterns (tool use, agentic RAG, planning, multi-agent, metacognition), production deployment, protocols (MCP, A2A), context engineering, memory management, and security. Each lesson includes a written tutorial, a short video, and Python code samples using Microsoft Agent Framework with Microsoft Foundry Agent Service V2. The repository supports 50+ language translations and includes a Discord community for learners to get help.
This repository provides educational content in the form of 18 markdown lessons, each with a README tutorial and Python code in the code_samples folder. The code demonstrates building agents with Microsoft Agent Framework and Microsoft Foundry Agent Service V2, covering patterns like agentic RAG, multi-agent collaboration, tool use, and more. Users configure Azure and Foundry services, run the code, and learn from practical examples. The repo also includes video links, extra learning resources, and automated multi-language translations via GitHub Actions.
- Beginners who want a structured, comprehensive path to learn AI agent development from scratch.
- Developers seeking to understand Microsoft Agent Framework and Foundry for building production agents.
- Educators looking for ready-made, multi-language course material for teaching AI concepts.
- Developers needing concrete code examples for agentic RAG, multi-agent patterns, etc., to reference in their own projects.
- Community learners who want to engage with others via Discord to ask questions and share progress.
How do you install or deploy this agent?
No installation needed; you can access the course content directly on GitHub. To clone the repository without translations (for a lighter download), use: git clone --filter=blob:none --sparse https://github.com/microsoft/ai-agents-for-beginners.git, then cd into the folder and run git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'.
How do you use this agent?
- Navigate to a lesson folder (e.g., 01-intro-to-ai-agents) and read the README tutorial, watch the video. 2. Set up an Azure account and Microsoft Foundry service as required. 3. Run the Python code in the code_samples folder, ensuring you have Python installed and dependencies configured. 4. Fork the repository to experiment and modify the code.
What are this agent's strengths and limitations?
- Structured curriculum covering 18 topics from intro to advanced, suitable for self-paced learning.
- Practical Python code examples using Microsoft's own agent framework, giving hands-on experience.
- Extensive multi-language support (50+) automatically updated, making it accessible globally.
- Includes videos and links to extra learning resources, enhancing the learning experience.
- Code samples are tied to Microsoft Agent Framework and Foundry, requiring an Azure account—potential vendor lock-in.
- The course is heavily Microsoft-centric; developers preferring other frameworks may find it less relevant.
- It is a tutorial only, not a deployable application; you must build your own agents based on what you learn.
- Some advanced lessons (e.g., browser automation) may require additional setup or dependencies.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| AI Agents for Beginners This agent | 46 · Major gaps | FrameworkFree + model costs | ★ 77k | 1d ago | Jupyter Notebook | — |
| Hugging Face Agents Course | 29 · Major gaps | Web appFree | ★ 34k | 13d ago | MDX | — |
| Microsoft Agent Framework Hands-On Samples | 50 · Major gaps | Library / SDKFree + model costs | ★ 385 | 4mo ago | Jupyter Notebook | — |
| Kite Agent Framework | 51 · Major gaps | CLIFree + model costs | ★ 11 | 3mo ago | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
The evidence shows this is an educational course repository, not a deployable Agent product. There is no implementation of least privilege, user confirmation, data flow transparency, sensitive data handling, dependency security, external effects, rollback, or source attribution. All trust criteria score 0 because there is no evidence these mechanisms exist.
Self-consistency: The course structure is clear, and lesson READMEs and code samples correspond without contradictions. Dependency availability: requirements.txt lists dependencies with pinned versions, but no lock file is provided, so availability is moderate. Failure messages: Code samples may include error handling, but it is not explicitly shown in the provided files, so score is low.
Audience and scenarios: Clearly targets beginners with 18 lessons covering various scenarios. Capability boundaries: The course describes the frameworks and services used, but does not define Agent capability boundaries. Trigger precision: As a course, there is no explicit trigger mechanism. Environment fit: Multi-language support and local clone guidance are provided, but detailed environment configuration is not fully covered.
Information architecture: README provides a clear course outline and navigation. Install notes: Clone and sparse checkout instructions are provided, but detailed installation steps are missing. Naming stability: Course names and directory structure are stable. Examples and FAQ: Extensive code examples and extra learning resources are provided, but no FAQ. Known limitations: Not explicitly listed. License: MIT license is clear. Versioning/changelog: Not provided. Maintenance responsibility: Maintained by Microsoft with contribution guidelines.
Output usability: Course content is directly usable for learning, and code samples are runnable. Marginal value: As an educational course, it provides a systematic learning path with high value. Cost-benefit: Free and open source, low cost, high benefit.
Claim traceability: Claims in the course are mostly supported by links. Cross-source corroboration: Some content has external links, but no independent verification. Fact-inference separation: Course content distinguishes facts and teaching suggestions, but not explicitly labeled.
- Not found in source: least-privilege scopingGrant only what the task needs: a dedicated account or read-only token, scoped to specific directories and repos.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: data-flow disclosureWatch which external services it contacts (proxy or firewall logs) and keep sensitive data out until you know where it goes.
- Not found in source: sensitive-data handlingUse dedicated, low-privilege, revocable API keys — never production credentials — and keep secrets out of logs.
- Not found in source: dependency securityPin versions and run a dependency audit (npm audit, pip-audit) before installing; prefer running it in a container.
- Not found in source: disclosed external effectsEstablish which external systems it writes to, sends to or changes, and verify with test accounts or repos before production.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Not found in source: verifiable attributionInstall from the official repo or registry and check the publisher and URL to avoid look-alike packages.
- This repository is an educational course, not a deployable Agent product, so trust-related criteria are not applicable.
- Dependencies are pinned but no lock file is provided, posing potential dependency availability risks.
- Known limitations and changelog are not provided; users need to assess on their own.