Overview

Go beyond simple prompts and learn to design robust, Azure-native AI agents. In this course, you’ll use Microsoft Foundry and the Agent Service to build role-based agents that reason, plan, and act with real-world tools and data. You’ll master advanced prompting techniques—including Chain-of-Thought, ReAct, and systematic prompt refinement—to turn raw model capabilities into reliable, controllable behavior. You'll chain agents into multi-step workflows, adding validation gates and feedback loops so agents can inspect, correct, and improve their outputs. Through focused, hands-on labs, you’ll apply these patterns to a realistic supply chain scenario, culminating in a multi-tool agent that can analyze data, call utilities, and support complex operational decisions end to end.

Syllabus

Introduction to Prompting for Effective LLM Reasoning and Planning with Microsoft Foundry

Introduces the core concepts of Agentic AI, the course structure, prerequisites, and learning environment.

The Role of Prompting in Agentic AI

Learn what AI Agents are and how they work. Understand the critical role prompting plays in guiding them to reason, plan, and act to achieve goals.

Role-Based Prompting

Explains the theory of using roles or personas to control the tone, style, and expertise of an LLM's output.

Implementing Role-Based Prompting with Microsoft Foundry

Explore how to implement role-based prompting in Azure Agent Service, including configuration, best practices, and use cases for tailored conversational AI experiences.

Chain-of-Thought and ReACT Prompting

Explains the conceptual frameworks for Chain-of-Thought (CoT) for guided reasoning and ReAct (Reason+Act) for enabling agents to plan and take actions.

Applying COT and ReACT Prompting with Microsoft Foundry Agent Service

Explore how to implement Chain-of-Thought (COT) and ReACT prompting in Azure AI Foundry Agent Service.

Prompt Instruction Refinement

Explains the theory of systematically refining prompt instructions by modifying components like Role, Task, Context, Examples, and Output Format.

Applying Prompt Instruction Refinement with Microsoft Foundry

Explore how to refine prompt instructions using Azure AI Foundry Agent Service for effective AI-driven solutions.

Chaining Prompts for Agentic Reasoning

Explains the conceptual framework for building multi-step AI workflows by linking the output of one prompt to the input of the next, and the importance of validation.

Chaining Prompts with Microsoft Foundry

Explore how to create and chain prompts using the Azure AI Foundry Agent Service to build dynamic, multi-step conversational AI workflows.

LLM Feedback Loops

Explains the conceptual framework for building self-improving systems where an agent uses feedback from its own actions to iteratively refine its output.

LLM Feedback Loops with Microsoft Foundry

Explore how to implement and manage feedback loops in LLM applications using Azure Foundry AI Agent Service for model improvement and reliability.

Project: AeroTurbine Solutions: Building an Agentic Supply Chain

Build a multi-agent supply chain workflow in Microsoft AI Foundry that generates BOMs, checks inventory, analyzes suppliers, and creates purchase orders, validated through resources and test runs.