Force7 Training
FRCMIC-4Microsoft

Develop AI Agents on Azure (AI-3026)

This course teaches developers how to design, build, deploy, and manage AI agents using Microsoft Azure AI services.

Duration · 1 dayVirtual + In-PersonInstructor-Led

Course Description

This course teaches developers how to design, build, deploy, and manage AI agents using Microsoft Azure AI services. Participants will learn how AI agents leverage Large Language Models (LLMs), tools, memory, orchestration, and enterprise data to automate tasks, support decision-making, and enhance business processes. The course emphasizes practical implementation, responsible AI, and production-ready agent architectures.

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Audience Profile

This course is intended for:

  • Developers
  • AI Engineers
  • Software Engineers
  • Cloud Architects
  • Solution Architects
  • Technical Professionals

Prerequisites

Before enrolling, you should have:

  • Basic programming experience
  • Familiarity with Azure services
  • Understanding of REST APIs and JSON
  • Foundational knowledge of Generative AI concepts
  • Recommended: Azure AI Fundamentals (AI-900)

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Understand AI agent architectures and capabilities
  • 2Build AI agents using Azure AI services
  • 3Integrate agents with enterprise data and external tools
  • 4Implement memory and contextual reasoning
  • 5Orchestrate agent workflows and actions
  • 6Apply responsible AI and security controls
  • 7Monitor, evaluate, and optimize agent performance
  • 8Deploy production-ready AI agent solutions

— Day-by-Day —

Course Outline

Module 1

Introduction to AI Agents and Azure AI

  • What Are AI Agents?
  • Agent vs. Chatbot Architectures
  • Generative AI Foundations
  • Large Language Models and Reasoning
  • Agent Capabilities and Use Cases
  • Azure AI Foundry Overview
  • Azure OpenAI Service Overview
  • Business Applications for AI Agents
  • Demonstration: Exploring AI Agent Scenarios in Azure

Module 2

Designing AI Agent Architectures

  • Core Agent Components
  • Planning and Reasoning Models
  • Tool Usage and Function Calling
  • Agent Decision-Making Processes
  • Multi-Step Task Execution
  • Single-Agent vs. Multi-Agent Architectures
  • Agent Workflow Design Patterns
  • Enterprise Use Cases
  • Hands-On Lab: Designing an AI Agent Solution

Module 3

Building AI Agents with Azure AI Services

  • Azure AI Agent Services
  • Agent Configuration
  • Model Selection Considerations
  • Prompt Design for Agents
  • Defining Agent Roles and Objectives
  • Action and Tool Integration
  • Testing Agent Behavior
  • Managing Agent Sessions
  • Hands-On Lab: Creating an AI Agent in Azure

Module 4

Integrating Data, Knowledge, and Tools

  • Retrieval-Augmented Generation (RAG)
  • Azure AI Search Integration
  • Knowledge Grounding Techniques
  • Connecting Databases and Business Systems
  • API Integration Concepts
  • Function Calling and Actions
  • Managing Data Sources
  • Enterprise Knowledge Management
  • Hands-On Lab: Integrating an Agent with Organizational Data

Module 5

Agent Memory and Context Management

  • Short-Term and Long-Term Memory
  • Conversation History Management
  • Context Windows
  • User Personalization Concepts
  • Session Management
  • State Tracking Techniques
  • Knowledge Retention Strategies
  • Memory Optimization
  • Hands-On Lab: Implementing Contextual Memory

Module 6

Responsible AI, Security, and Governance

  • Responsible AI Principles
  • Agent Safety and Risk Management
  • Prompt Injection and Threat Mitigation
  • Data Privacy and Protection
  • Identity and Access Controls
  • Compliance Considerations
  • Human Oversight and Escalation Models
  • AI Governance Frameworks
  • Discussion: Agent Security and Ethical Considerations

Module 7

Monitoring, Evaluation, and Optimization

  • Agent Evaluation Techniques
  • Measuring Accuracy and Relevance
  • User Satisfaction Metrics
  • Logging and Monitoring
  • Performance Optimization
  • Cost Management Strategies
  • Continuous Improvement Processes
  • Production Support Considerations
  • Hands-On Lab: Evaluating and Optimizing Agent Performance

Module 8

Deploying AI Agents to Production

  • Deployment Architectures
  • Production Readiness Assessments
  • Scalability Considerations
  • High Availability Planning
  • Monitoring and Alerting
  • Change Management
  • Lifecycle Management
  • Enterprise Adoption Strategies
  • Hands-On Lab: Deploying an Enterprise AI Agent Solution
  • Capstone Exercise: Build an Enterprise AI Agent
  • Capstone Exercise: Participants will design and implement an AI agent capable of:
  • Capstone Exercise: Understanding user requests
  • Capstone Exercise: Accessing organizational knowledge
  • Capstone Exercise: Performing actions through connected tools
  • Capstone Exercise: Maintaining conversation context
  • Capstone Exercise: Following governance and security requirements
  • Activity: Define business requirements
  • Activity: Design agent workflows
  • Activity: Configure data integrations
  • Activity: Implement memory and context management
  • Activity: Apply responsible AI controls
  • Activity: Present solution architecture

The Big Picture

Key Takeaways

  • AI agent architecture fundamentals
  • Azure AI agent development
  • Tool and data integration
  • Memory and context management
  • Responsible AI implementation
  • Agent monitoring and optimization
  • Production deployment strategies

What You'll Walk Away With

Skills Gained

  • AI agent design and development
  • Azure AI service integration
  • Retrieval-Augmented Generation (RAG)
  • Function calling and tool orchestration
  • Agent security and governance
  • Production deployment and management

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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