Overview

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.

Syllabus

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.