[![Intro to AI Agents](./images/lesson-1-thumbnail.png)](https://youtu.be/3zgm60bXmQk?si=QA4CW2-cmul5kk3D) > _(Click the image above to watch the video for this lesson)_ # Introduction to AI Agents and Agent Use Cases Welcome to the **AI Agents for Beginners** course! This course gives you the foundational knowledge — and real working code — to start building AI Agents from scratch. Come say hi in the Azure AI Discord Community — it's full of learners and AI builders who are happy to answer questions. Before we jump into building, let's make sure we actually understand what an AI Agent *is* and when it makes sense to use one. --- ## Introduction This lesson covers: - What AI Agents are, and the different types that exist - Which kinds of tasks AI Agents are best suited for - The core building blocks you'll use when designing an Agentic solution ## Learning Goals By the end of this lesson, you should be able to: - Explain what an AI Agent is and how it's different from a regular AI solution - Know when to reach for an AI Agent (and when not to) - Sketch out a basic Agentic solution design for a real-world problem --- ## Defining AI Agents and Types of AI Agents ### What are AI Agents? Here's a simple way to think about it: > **AI Agents are systems that let Large Language Models (LLMs) actually *do things* — by giving them tools and knowledge to act on the world, not just respond to prompts.** Let's unpack that a bit: - **System** — An AI Agent isn't just one thing. It's a collection of parts working together. At its core, every agent has three pieces: - **Environment** — The space the agent works in. For a travel booking agent, this would be the booking platform itself. - **Sensors** — How the agent reads the current state of its environment. Our travel agent might check hotel availability or flight prices. - **Actuators** — How the agent takes action. The travel agent might book a room, send a confirmation, or cancel a reservation. ![What Are AI Agents?](./images/what-are-ai-agents.png) - **Large Language Models** — Agents existed before LLMs, but LLMs are what make modern agents so powerful. They can understand natural language, reason about context, and turn a vague user request into a concrete plan of action. - **Perform Actions** — Without an agent system, an LLM just generates text. Inside an agent system, the LLM can actually *execute* steps — searching a database, calling an API, sending a message. - **Access to Tools** — What tools the agent can use depends on (1) the environment it's running in and (2) what the developer chose to give it. A travel agent might be able to search flights but not edit customer records — it's all about what you wire up. - **Memory + Knowledge** — Agents can have short-term memory (the current conversation) and long-term memory (a customer database, past interactions). The travel agent might "remember" that you prefer window seats. --- ### The Different Types of AI Agents Not all agents are built the same. Here's a breakdown of the main types, using a travel booking agent as the running example: | **Agent Type** | **What It Does** | **Travel Agent Example** | |---|---|---| | **Simple Reflex Agents** | Follows hard-coded rules — no memory, no planning. | Sees a complaint email → forwards it to customer service. That's it. | | **Model-Based Reflex Agents** | Keeps an internal model of the world and updates it as things change. | Tracks historical flight prices and flags routes that are suddenly expensive. | | **Goal-Based Agents** | Has a goal in mind and figures out how to reach it step by step. | Books a full trip (flights, car, hotel) starting from your current location to get you to your destination. | | **Utility-Based Agents** | Doesn't just find *a* solution — finds the *best* one by weighing tradeoffs. | Balances cost vs. convenience to find the trip that scores highest for your preferences. | | **Learning Agents** | Gets better over time by learning from feedback. | Adjusts future booking recommendations based on post-trip survey results. | | **Hierarchical Agents** | A high-level agent breaks work into subtasks and delegates to lower-level agents. | A "cancel trip" request gets split into: cancel flight, cancel hotel, cancel car rental — each handled by a sub-agent. | | **Multi-Agent Systems (MAS)** | Multiple independent agents working together (or competing). | Cooperative: separate agents handle hotels, flights, and entertainment. Competitive: multiple agents compete to fill hotel rooms at the best price. | --- ## When to Use AI Agents Just because you *can* use an AI Agent doesn't mean you always *should*. Here are the situations where agents really shine: ![When to use AI Agents?](./images/when-to-use-ai-agents.png) - **Open-Ended Problems** — When the steps to solve a problem can't be pre-programmed. You need the LLM to figure out the path dynamically. - **Multi-Step Processes** — Tasks that require using tools across several turns, not just a single lookup or generation. - **Improvement Over Time** — When you want the system to get smarter based on user feedback or environmental signals. We'll dig deeper into when (and when *not*) to use AI Agents in the **Building Trustworthy AI Agents** lesson later in the course. --- ## Basics of Agentic Solutions ### Agent Development The first thing you do when building an agent is define *what it can do* — its tools, actions, and behaviors. In this course, we use the **Microsoft Foundry Agent Service** as our main platform. It supports: - Models from providers like OpenAI, Mistral, and Meta (Llama) - Licensed data from providers like Tripadvisor - Standardized OpenAPI 3.0 tool definitions ### Agentic Patterns You communicate with LLMs through prompts. With agents, you can't always hand-craft every prompt manually — the agent needs to take action across many steps. That's where **Agentic Patterns** come in. They're reusable strategies for prompting and orchestrating LLMs in a more scalable, reliable way. This course is structured around the most common and useful agentic patterns. ### Agentic Frameworks Agentic Frameworks give developers ready-made templates, tools, and infrastructure for building agents. They make it easier to: - Wire up tools and capabilities - Observe what the agent is doing (and debug when it goes wrong) - Collaborate across multiple agents In this course, we focus on the **Microsoft Agent Framework (MAF)** for building production-ready agents. --- ## Code Samples Ready to see it in action? Here are the code samples for this lesson: - 🐍 Python: [Agent Framework](./code_samples/01-python-agent-framework.ipynb) - 🔷 .NET: [Agent Framework](./code_samples/01-dotnet-agent-framework.md) --- ## Got Questions? Join the [Microsoft Foundry Discord](https://discord.com/invite/ATgtXmAS5D) to connect with other learners, attend office hours, and get your AI Agent questions answered by the community. --- ## Smoke-Testing This Agent (Optional) Once you learn to deploy agents in [Lesson 16](../16-deploying-scalable-agents/README.md), you can add a fast post-deploy health check for this lesson's `TravelAgent` with the ready-made catalog [`tests/lesson-01-smoke-tests.json`](../tests/lesson-01-smoke-tests.json). See [`tests/README.md`](../tests/README.md) for how to run it. --- ## Previous Lesson [Course Setup](../00-course-setup/README.md) ## Next Lesson [Exploring Agentic Frameworks](../02-explore-agentic-frameworks/README.md)