Overview

This course offers a comprehensive exploration of designing and implementing multi-agent architectures. Starting with an introduction to course concepts, students will learn to leverage the Semantic Kernel for effective agent architecture creation. Key lessons focus on agent orchestration, optimizing routing and data flow, and managing state within multi-agent environments. Advanced topics include orchestrating agents collaboratively and employing Retrieval Augmented Generation (RAG) to enhance data retrieval in agentic systems. The course culminates with a practical application of these concepts in the VectraBank project, showcasing agentic RAG tailored for banking applications.

Syllabus

Introduction to Multi-Agent AI Systems with Microsoft Azure

Get introduced to multi-agent systems, their real-world applications with Azure, course goals, and meet the expert instructors guiding your learning journey.

Designing Multi-Agent Architecture

Explain the core components of multi-agent systems and how to design their high-level architecture.

Creating Multi-Agent Designs

Explain the core components of multi-agent systems and how to design their high-level architecture.

Multi-Agent Architecture with Python

Develop a multi-agent system by coding the designed architecture and connecting agents with well-defined interfaces.

Implementing Multi-Agent Architecture with Semantic Kernel

Learn to build multi-agent AI systems with Semantic Kernel in Python, orchestrating specialized agents in parallel or sequence to solve complex smart city planning problems.

Orchestrating Agent Activities

Apply orchestration techniques to coordinate multiple agent actions and achieve complex workflows.

Implementing Agent Orchestration with Semantic Kernel

Learn to orchestrate specialized AI agents with Semantic Kernel using sequential, parallel, and conditional patterns for flexible, efficient multi-agent solutions.

Routing and Data Flow in Agentic Systems

Configure routing mechanisms to manage data flow among agents in multi-agent systems.

Implementing Routing and Data Flow in Agentic Systems with Semantic Kernel

Learn to build agentic systems with Semantic Kernel, using Azure SQL for persistent data, intelligent routing, and real-time, context-aware multi-agent responses.

State Management in Multi-Agent Systems

Evaluate methods for tracking and updating agent state across multi-turn interactions.

Implementing State Management in Multi-Agent Systems with Semantic Kernel

Learn to build collaborative multi-agent AI systems with Semantic Kernel, using shared state management and plugins for real-time, coordinated agent workflows.

Multi-Agent Orchestration and State Coordination

Develop a coordinated multi-agent system that synchronizes states for coherent task execution.

Implementing Multi-Agent Orchestration and State Coordination with Semantic Kernel

Learn to build multi-agent systems with a coordinator agent, shared state, and Semantic Kernel plugins for dynamic orchestration and workflow optimization, using a pasta factory simulation.

Multi-Agent Retrieval Augmented Generation

Extend RAG to multiple cooperating agents, each specialized in certain retrieval tasks.

Implementing Multi-Agent Retrieval Augmented Generation with Semantic Kernel

Learn to build and extend a multi-agent RAG system using Semantic Kernel, integrating specialized AI agents for robust, in-depth research with error handling and report persistence.