Module Introduction

Welcome to the module Getting Started with Generative AI and Agents. This lesson builds a practical foundation in generative AI: what generative models are, how they differ from traditional AI approaches, and how AI agents combine models, tools, and knowledge to solve real tasks. We start with core concepts, move into the enabling technologies, and finish with hands‑on examples using Microsoft Foundry (Azure AI Foundry).

This module is designed to help you progress from conceptual understanding to practical application. Expect concise explanations, clear examples, and step-by-step guidance for beginning to build generative AI applications and agents on Azure AI Foundry.

By the end of this module you will be able to:

Learning Objective	What you will understand or do	Example outcome
Explain the fundamentals of generative AI	Distinguish generative models from discriminative/traditional models and know common use cases	Describe how a language model generates text vs. a classifier predicting labels
Describe types of generative AI models and their capabilities	Identify models (e.g., LLMs, multimodal models), their intended tasks, and typical strengths/limitations	Choose the right model family for text generation, summarization, or multimodal tasks
Understand AI agents and how they combine models, tools, and knowledge	Explain the agent architecture and how tools/APIs augment model capabilities	Design a simple agent that uses an LLM plus a retrieval tool for question answering
Start building generative AI applications and agents using Microsoft Foundry	Apply these concepts in an Azure AI Foundry context to prototype solutions	Create a minimal agent-based workflow that integrates a model and an external API

A slide titled "Learning Objectives" showing four numbered goals down the right side. The objectives cover explaining the fundamentals of Generative AI, understanding different generative AI models and AI agents, and getting started building generative AI applications and agents using Azure AI Foundry.

Module flow

Conceptual foundation: key terms, model categories (LLMs, diffusion, multimodal), and common generative AI tasks.
Technology primer: how models are trained and served, tool integrations, and agent orchestration patterns.
Practical implementation: guided examples and patterns to build and evaluate generative AI applications and agents on Azure AI Foundry.

Recommended next steps

Follow the next lessons to see concrete examples and walkthroughs that connect these concepts to code and Foundry configuration.
Experiment with simple prompts and a basic agent pattern to reinforce the architecture described in the module.

Links and references

Microsoft Learn — Azure documentation: https://learn.microsoft.com/
Azure AI (product overview): https://azure.microsoft.com/en-us/products/ai/
Azure AI documentation (services & SDKs): https://learn.microsoft.com/azure/ai-services/

AI in Azure

Getting Started With AI

Getting Started With Generative AI and Agents

Getting Started With Text Analysis

Getting Started With AI Speech

Getting Started With Computer Vision

Getting Started With Information Extraction

Module Introduction

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