Agentic AI Workflows with Microsoft Azure

Name: Agentic AI Workflows with Microsoft Azure Course
Rating: 5 (1 reviews)

This course offers a comprehensive exploration of advanced workflow implementations using Azure's capabilities. Beginning with foundational concepts, you will learn about practical applications such as implementing agentic workflows using the Semantic Kernel, utilizing prompt chaining for enhanced interactions, and applying routing workflows for efficient task management. Additional focus will be on parallelization techniques with Python, as well as developing evaluator-optimizer workflows to refine results. Finally, you will learn about the orchestrator-workers pattern for seamless workflow coordination and leverage AgentQuant for insightful data analysis.

Course

Intermediate
15 hours
Updated: Dec 4, 2025

Subscription · Monthly

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Unlimited access to hundreds of top-rated courses
Hands-on projects with expert feedback
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Program Certificates

Skills you'll learn

12 skills

Implementing Agent Orchestration
Sequential Task Decomposition for Prompt Chaining
Agentic Iterative Refinement Workflow Design
Implementing Sequential Task Prompt Chain Workflows
Agentic Task Routing Design

Prerequisites

4 prerequisites

Prior to enrolling, you should have the following knowledge:

CoT and ReAct
Implementing Role-Based Prompting
Iterative Prompt Development
Basic Python

You will also need to be able to communicate fluently and professionally in written and spoken English.

Course Outline

16 lessons
1 project

Agentic AI Workflows with Microsoft Azure

Introduction to Building AI Agents with Microsoft Azure
Discover the basics of agentic workflows with Azure, using Python and the Semantic Kernel SDK to build adaptive AI systems powered by large language models.
Understanding Agentic Workflows
Explores what defines a modern AI agent, its core components (Persona, Knowledge, Tools, Interaction), and the different types of agents based on their LLM interaction model.
Agentic Workflow Modeling
Design and visualize agentic workflows. Learn common agent types as building blocks for creating visual workflow diagrams.
Implementing Agentic Workflow Modeling
Design and visualize agentic workflows. Learn common agent types as building blocks for creating visual workflow diagrams.
Agentic Workflow Implementation with Semantic Kernel
Learn to implement a multi-agent workflow in Semantic Kernel, creating specialized agents to efficiently route and answer customer inquiries by domain expertise.
Agentic Workflow Patterns: Prompt Chaining Workflow
Introduces the Prompt Chaining pattern for breaking down complex tasks into a sequence of smaller, dependent steps. It covers strategies for task decomposition, validation, and context management.
Implementing Agentic Prompt Chaining Workflows with Semantic Kernel
Learn to solve complex problems by chaining specialized AI agents in sequence using Semantic Kernel, plugins, and prompt workflows to build comprehensive solutions in Python.
Agentic Workflow Patterns: Routing
Teaches the Routing pattern, which involves classifying incoming tasks and directing them to the most appropriate specialized agent or processing path.
Implementing Agentic Routing Workflows with Semantic Kernel
Learn to build multi-agent AI workflows with orchestrators in Semantic Kernel, intelligently routing tasks to specialized agents for scalable, robust automation.
Agentic Workflow Patterns: Parallelization
Introduces the Parallelization pattern for executing multiple agent tasks concurrently. It covers strategies for task decomposition (sharding, aspect-based) and result aggregation.
Implementing Agentic Parallelization Workflows with Semantic Kernel
Learn to speed up data analysis by building a multi-agent Python workflow with Semantic Kernel, using asyncio and ConcurrentOrchestration for parallel agent execution.
Agentic Workflow Patterns: Evaluator-Optimizer Workflow
Focuses on the Evaluator-Optimizer pattern, an iterative process of generation, critique, and refinement to improve output quality. It emphasizes clear evaluation criteria and actionable feedback.
Implementing Agentic Evaluator-Optimizer Workflows with Semantic Kernel
Learn to automate iterative content refinement using agentic creator-critic workflows with Semantic Kernel, orchestrating agents for high-quality, reliable AI-generated outputs.
Agentic Workflow Patterns Orchestrator-Workers Workflow
Introduces the advanced Orchestrator-Workers pattern, where a central agent dynamically plans, delegates, and synthesizes the work of multiple specialized worker agents.
Implementing Agentic Orchestrator-Workers Pattern with Semantic Kernel
Learn to implement the agentic orchestrator-workers pattern in Semantic Kernel, enabling multiple AI agents to plan, delegate tasks, and collaborate for complex problem solving.

Project: AgentQuant: Agentic Data Analysis
AI-driven workflow that cleans, analyzes, visualizes CSV data, and generates reports using asynchronous Semantic Kernel agents with human-in-the-loop validation and dynamic Python code execution.

Program Instructors

1 instructor

Unlike typical professors, our instructors come from Fortune 500 and Global 2000 companies and have demonstrated leadership and expertise in their professions:

Peter Kowalchuk

Engagement Director at C3.ai

Peter Kowalchuk

Engagement Director at C3.ai

About this program

Master Semantic Kernel’s code-first approach for Azure agents. Build dynamic, multi-step workflows with branching and orchestration.

Subscription · Monthly

Cancel Anytime
Unlimited access to hundreds of top-rated courses
Hands-on projects with expert feedback
Personalized career coaching and interview prep
Program Certificates

Agentic AI Workflows with Microsoft Azure

Skills you'll learn

Prerequisites

Course Outline

Program Instructors

About this program

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