Architecting agentic AI business solutions is an advanced course for architects, senior consultants, and technical leaders responsible for planning, designing, and governing AI-powered enterprise solutions built on Microsoft platforms. This course serves as a foundational, real-world, and architectural preparation step that builds the design judgment, strategic reasoning, and end-to-end understanding learners need before pursuing the AB‑100 exam or implementing agentic AI solutions at scale. Learners will explore how to architect AI-powered business solutions that use agents, copilots, and generative AI to automate tasks, improve decision-making, and enhance employee and customer experiences. Emphasis is placed on architecture, trade-offs, governance, cost/benefit analysis, and lifecycle management, rather than step-by-step configuration. 

Learning Path 1: Introduction to Agentic AI Business Solution Architecture

• Introduction

• Role of the Architect in AI Transformation for Businesses

• Overview of Microsoft AI Technologies

• Identify Out-of-the-Box (OOB) Microsoft AI Agent Resources

• Identify Out-of-the-Box (OOB) Microsoft AI Agents

Learning Path 2: Analyze requirements for AI-powered business solutions

• Introduction

• Assess the Use of Agents in Task Automation, Data Analytics, and Decision-Making

• Review Data for Grounding (Accuracy, Relevance, Timeliness, Cleanliness, Availability)

• Organize Business Solution Data to Be Available for Other AI Systems

Learning Path 3: Design overall AI strategy for business solutions

• Introduction

• Implement the AI Adoption Process from the Cloud Adoption Framework for Azure

• Design the Strategy for Building AI Agents in Business Solutions

• Design a Multi-Agent Solution

• Develop the Use Cases for Prebuilt Agents in the Solution

• Define the solution rules and constraints when building AI components (with Copilot Studio, Microsoft Foundry, and Foundry Tools)

• Determine the use of generative AI and knowledge sources in agents built with Copilot Studio

• Determine When to Build Custom Agents or Extend Microsoft 365 Copilot

• Determine When Custom AI Models Should Be Createdd

• Provide guidelines for creating a prompt library

• Develop the Use Cases for Customized Small Language Models

• Prompt Engineering for AI-Powered Business Solutions

• Identify Key Business User Roles for AI Workloads

• Evaluate Regional and Local AI and Data Regulation and Compliance Requirements

Learning Path 4: Evaluate the costs and benefits of an AI-powered business solution

• Introduction

• Select ROI Criteria for AI-Powered Business Solutions Including Total Cost of Ownership

• Create an ROI Analysis for the Proposed AI Solution

• Analyze Whether to Build, Buy, or Extend AI Components for Business Solutions

• Implement a model router to intelligently route requests to the most suitable model

Learning Path 5: Design AI and agents for business solutions

• Introduction

• Define the Core Tenets of Microsoft’s Responsible AI Guidelines for AI Business Solutions

• Design Business Terms for Copilot in Dynamics 365 Apps for Customer Experience and Service

• Design Customizations of Copilot in Dynamics 365 Apps for Customer Experience and Service

• Design Connectors for Copilot in Dynamics 365 Sales

• Design Agents for Integration With Dynamics 365 Contact Center Channels

• Design Task Agents

• Design Autonomous Agents

• Design Prompt and Response Agents

• Propose Foundry Tools for a Given Requirement

• Propose Code First Generative Pages and the Use of an Agent Feed for Apps

• Design Topics for Copilot Studio, Including Fallback

• Design data processing for AI models and grounding

• Design a Business Process to Include AI Components in a Power Apps Canvas App

• Apply the Microsoft Power Platform Well-Architected Framework to Intelligent Application Workloads

• Determine When to Use Standard Natural Language Processing, Azure Conversational Language Understanding, or Generative AI Orchestration in Copilot Studio

• Design Agents and Agent Flows with Copilot Studio

• Design Prompt Actions in Copilot Studio

• Define Success Criteria and Adoption Goals for AI Business Solutions

Learning Path 6: Design extensibility of AI solutions

• Introduction

• Design AI solutions by using custom models in Microsoft Foundry

• Design agents in Microsoft 365 Copilot

• Design agent extensibility in Copilot Studio

• Design agent extensibility with Model Context Protocol in Copilot Studio

• Design Agents to automate tasks in apps and websites by using Computer Use in Copilot Studio

• Design agent behaviours in Copilot Studio, including reasoning and voice mode

• Optimize solution design by using agents in Microsoft 365, including Teams and SharePoint

Learning Path 7: Orchestrate configuration for prebuilt agents and apps

• Introduction

• Orchestrate AI features in Dynamics 365 apps for finance and supply chain

• Design AI solutions in Dynamics 365 apps for customer experience & service

• Propose Microsoft 365 Agents for Business Scenarios

• Orchestrate the Configuration of Microsoft 365 Copilot for Sales and Microsoft 365 Copilot for Service

• Propose Microsoft Power Platform AI features, including AI Hub

• Design interoperability of the finance and operations agent chats to use additional knowledge sources

• Recommend the process of adding knowledge sources to in app help and guidance for Dynamics 365 Finance or Dynamics 365 Supply Chain Management apps

Learning Path 8: Analyze, monitor, and tune AI-powered business solutions

• Introduction

• Recommend the Process and Tools for Monitoring Agents

• Analyze backlog and user feedback of AI and agent usage

• Apply AI Based Tools to Analyze and Identify Issues and Perform Tuning

• Monitor agent performance and metrics

• Interpret telemetry data for performance and model tuning

Learning Path 9: Manage the testing of AI-powered business solutions

• Introduction

• Recommend the process and metrics to test agents

• Create validation criteria of custom AI models

• Validate effective Copilot prompt best practices

• Design end-to-end test scenarios of AI solutions that use multiple Dynamics 365 apps

• Build the strategy for creating test cases by using Copilot

Learning Path 10: Design the ALM process for AI-powered business solutions

• Introduction

• Design the ALM process for data used in AI models and agents

• Design the ALM process for Copilot Studio agents, connectors, and actions

• Design the ALM process for Microsoft Foundry agents

• Design the ALM process for custom AI models

• Design the ALM process for AI in Dynamics 365 apps for finance and supply chain

• Design the ALM process for AI in Dynamics 365 apps for customer experience and service

Learning Path 11: Design responsible AI, security, governance, risk management, and compliance

• Introduction

• Design security for agents

• Design governance for agents

• Design model security

• Analyze solution and AI vulnerabilities and mitigations, including prompt manipulation

• Review solution for adherence to responsible AI principles

• Validate data residency and movement compliance

• Design access controls on grounding data and model tuning

• Design audit trails for changes to models and data