Agents for Developers

In this course, you will be introduced to AI agents and quickly move from concepts to hands-on implementation. You’ll learn how to design agents that follow robust, precise instructions so they act purposefully and are harder to trick or misuse. We’ll explore applications in data and developer workflows — from code generation and execution to complex matching and reasoning tasks. You’ll also see how agents can efficiently retrieve knowledge using Retrieval-Augmented Generation (RAG). Beyond the technical side, we’ll discuss the social, ethical and legal aspects of working with agents. All implementations are done in Azure AI Foundry, a secure Microsoft-managed environment that supports a wide range of AI models.

What you learn

• The fundamentals of AI agents and their practical applications.

• How to write effective, robust and secure instructions (prompts) for agents.

• How agents retrieve and use knowledge through RAG (Retrieval-Augmented Generation).

• How to implement and integrate agents in a developer context using Azure AI Foundry.

• The social, ethical and legal dimensions of working with AI agents.

After this course you will be able to:

• Independently implement an AI agent in Azure AI Foundry.

• Add precise, reliable and safe instructions to your agent.

• Use RAG-based retrieval to ground your agent in domain-specific knowledge.

• Apply agents to real-world data and programming tasks.

For whom

• Developers.

• Data engineers.

• Data scientists.

Prerequisites

• Basic Python knowledge (with a local Python installation available).

• Prior experience with ChatGPT or other language models is recommended; using ChatGPT a few times per week is sufficient.

Content (global program)

Part 1

• Introduction to AI and ChatGPT.

Part 2

• Introduction to Agents.

• Core concepts, capabilities and limitations.

Part 3

• Building and Testing Agents (Exercise).

• Hands-on implementation in Azure AI Foundry.

Part 4

• Societal Context for Agents and RAG Models.

• Ethical, social and legal considerations.

Part 5

• Multi Agents 1.

• Coordinating multiple agents and division of roles.

Part 6

• Multi Agents 2 (Open Exercise).

• Designing and implementing your own multi-agent scenario.

Part 7

• Questions and Closing.

• Recap, discussion of use cases and next steps.