Microsoft Azure AI Agent Development (AI-3026) with Azure AI Agent Service and Semantic Kernel

This practical AI-3026 Azure AI Agent Development course is designed for developers, AI engineers, cloud professionals, solution architects, and technology enthusiasts who want to build intelligent AI agents using Microsoft Azure. The course provides a comprehensive introduction to modern agentic AI technologies and demonstrates how Azure AI services can be used to create scalable, enterprise-ready AI agent solutions.

Students will learn how AI agents operate, how they interact with users and external systems, and how Azure AI Agent Service enables organizations to build intelligent applications capable of autonomous task execution, reasoning, and workflow automation. The curriculum combines foundational concepts with hands-on implementation techniques, helping learners design, develop, deploy, and manage AI agents in real-world cloud environments.

The course also introduces Semantic Kernel for agent orchestration and explores multi-agent collaboration patterns that enable advanced business automation and intelligent decision-making systems.

1.1 Introduction to AI Agents on Azure

This section introduces the fundamentals of AI agents and explains how Azure supports modern agent-based applications.

1.1.1 Understanding AI Agents

Learn what AI agents are and how they differ from traditional AI applications and chatbots.

1.1.2 Agentic AI Concepts

Explore the core principles behind autonomous AI systems.

1.1.3 Business Applications of AI Agents

Discover how organizations use AI agents to automate workflows and improve productivity.

1.1.4 Azure AI Ecosystem Overview

Understand the Azure services that support agent development.

1.2 Azure AI Agent Service Fundamentals

This section introduces Azure AI Agent Service and its capabilities.

1.2.1 Introduction to Azure AI Agent Service

Learn the purpose and architecture of Azure's agent platform.

1.2.2 Core Components of Agent Service

Explore the building blocks used to create intelligent agents.

1.2.3 Agent Lifecycle Management

Understand how agents are created, configured, and maintained.

1.2.4 Agent Service Use Cases

Review practical enterprise applications powered by Azure AI Agents.

1.3 Building Your First Azure AI Agent

This section focuses on creating and configuring intelligent agents.

1.3.1 Agent Creation and Configuration

Learn how to build and customize AI agents.

1.3.2 Defining Agent Instructions

Create effective agent behaviors through structured guidance.

1.3.3 User Interaction Design

Develop conversational experiences for end users.

1.3.4 Testing and Validation

Evaluate agent performance and behavior.

1.4 Extending Agents with Tools

This section explores how AI agents can access external capabilities.

1.4.1 Tool Calling Fundamentals

Understand how agents invoke external tools and functions.

1.4.2 Custom Tool Development

Create specialized tools for business requirements.

1.4.3 Integrating External APIs

Connect agents to web services and third-party systems.

1.4.4 Enterprise Tool Management

Control and monitor tool usage securely.

1.5 Accessing Business Data with AI Agents

This section focuses on enabling agents to work with organizational data.

1.5.1 Connecting to Data Sources

Allow agents to access structured and unstructured data.

1.5.2 Knowledge Retrieval Systems

Build agents that retrieve relevant information dynamically.

1.5.3 Business Intelligence Integration

Use AI agents to support data-driven decision-making.

1.5.4 Secure Data Access

Implement secure and compliant data interactions.

1.6 Agent Workflow Automation

This section introduces workflow automation using AI agents.

1.6.1 Understanding Agent Workflows

Learn how agents execute multi-step tasks.

1.6.2 Process Automation Techniques

Automate repetitive business activities.

1.6.3 Intelligent Task Management

Enable agents to organize and prioritize work.

1.6.4 Workflow Optimization

Improve efficiency through intelligent automation.

1.7 Introduction to Semantic Kernel

This section explores Microsoft's Semantic Kernel framework for AI applications.

1.7.1 Semantic Kernel Fundamentals

Understand the purpose and capabilities of Semantic Kernel.

1.7.2 Kernel Architecture

Learn how Semantic Kernel organizes AI workflows.

1.7.3 AI Function Integration

Combine AI models with business logic and external functions.

1.7.4 Semantic Kernel Use Cases

Explore practical implementation scenarios.

1.8 Agent Orchestration with Semantic Kernel

This section focuses on coordinating complex agent workflows.

1.8.1 Workflow Orchestration Fundamentals

Manage interactions between multiple AI components.

1.8.2 Planning and Execution Strategies

Enable agents to organize and execute tasks intelligently.

1.8.3 Context and Memory Management

Improve agent performance through contextual awareness.

1.8.4 Advanced Orchestration Patterns

Build scalable and maintainable AI systems.

1.9 Multi-Agent Systems on Azure

This section introduces collaborative AI architectures.

1.9.1 Understanding Multi-Agent Systems

Learn how multiple agents cooperate to solve problems.

1.9.2 Agent Roles and Responsibilities

Assign specialized functions to different agents.

1.9.3 Agent Collaboration Models

Explore communication and teamwork patterns.

1.9.4 Distributed Agent Architectures

Design scalable multi-agent solutions.

1.10 Designing Enterprise AI Agent Solutions

This section focuses on building enterprise-ready AI systems.

1.10.1 Enterprise Architecture Principles

Understand large-scale AI solution design.

1.10.2 Scalability Considerations

Prepare systems for increasing workloads.

1.10.3 Reliability and Fault Tolerance

Build resilient AI applications.

1.10.4 Governance and Compliance

Implement organizational standards and controls.

1.11 Security for Azure AI Agents

This section explores security best practices for AI applications.

1.11.1 Identity and Access Management

Secure agent access to services and resources.

1.11.2 Data Protection Strategies

Safeguard sensitive information.

1.11.3 Secure Tool Execution

Prevent misuse of external capabilities.

1.11.4 Responsible AI Practices

Develop trustworthy and ethical AI systems.

1.12 Deploying and Managing AI Agents

This section covers deployment and operational management.

1.12.1 Agent Deployment Strategies

Publish AI agents to production environments.

1.12.2 Monitoring and Observability

Track performance and system health.

1.12.3 Agent Maintenance and Updates

Manage lifecycle improvements and enhancements.

1.12.4 Production Support Operations

Ensure long-term stability and reliability.

1.13 Real-World Azure Agent Solutions

This section demonstrates practical applications of Azure AI agents.

1.13.1 Customer Service Agents

Build intelligent support and assistance systems.

1.13.2 Business Process Automation

Automate operational workflows using AI.

1.13.3 Knowledge Management Systems

Create agents that retrieve and organize information.

1.13.4 Enterprise Productivity Assistants

Develop AI tools that improve workplace efficiency.

1.14 AI-3026 Skills and Certification Preparation

This section aligns course content with AI-3026 learning objectives.

1.14.1 Azure AI Agent Concepts Review

Reinforce key knowledge areas.

1.14.2 Agent Development Best Practices

Apply recommended implementation strategies.

1.14.3 Azure AI Service Integration Review

Consolidate platform knowledge.

1.14.4 Practical Readiness Assessment

Evaluate preparedness for real-world projects.

1.15 Final Skills and Learning Outcomes

By the end of this course, learners will be able to design, build, customize, orchestrate, deploy, and manage intelligent AI agents using Azure AI Agent Service, Semantic Kernel, and modern Azure AI technologies.

They will gain practical experience creating autonomous agents, integrating external tools and business data, designing multi-agent systems, automating workflows, and implementing enterprise-grade AI solutions capable of supporting modern business operations and intelligent decision-making processes.