← Back to repo search

Examples & learning

AI Agents for Beginners

microsoft/ai-agents-for-beginners

AI Agents for Beginners is Microsoft’s structured learning course covering agent concepts and implementation. It provides a coherent sequence for teams that would otherwise learn from disconnected demos and marketing claims.

THE PRACTICAL EXPLANATION

What this repository is

AI Agents for Beginners is Microsoft’s structured learning course covering agent concepts and implementation. It provides a coherent sequence for teams that would otherwise learn from disconnected demos and marketing claims.

WHERE TO USE IT

The work it fits

Use it for onboarding developers, product managers and technical stakeholders before an agent project begins, or as the basis of an internal learning programme.

WHO MAY USE IT

The people it suits

Beginners and mixed technical teams that need shared language around agents, tools, planning, memory and responsible implementation.

HOW TO USE IT

A sensible adoption path

Work through the lessons in order and run the exercises. After each section, connect the concept to one real organisational workflow and record the associated risks. Finish by producing a small controlled prototype rather than jumping directly to production.

  1. 01Agree on the course version and required setup.
  2. 02Complete lessons in a shared cohort.
  3. 03Relate each pattern to a real business process.
  4. 04Build and review one final bounded prototype.

GETTING THE BEST RESULTS

Use the repository with discipline

  • Use the course to create common vocabulary.
  • Separate learning examples from deployable architecture.
  • Include security and domain experts in the review.

WHY IT MAY BE USEFUL

The shortest useful assessment

A structured Microsoft course covering agent concepts and implementation.

Best considered for: Teams learning agent design from first principles.

READ BEFORE YOU ADOPT IT

The practical caution

Training examples are a starting point, not a production architecture.

Confirm the current licence, maintenance status, dependency risk, data path, model access, tool permissions and human approval points at the source. A public repository is inspectable raw material—not proof that a system is secure, supported or suitable for your production environment.