Course Overview
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.
Who should attend
This course is intended for experienced technology professionals who are responsible for planning, designing, and guiding AI-powered business solutions using Microsoft platforms. This course assumes familiarity with Microsoft business applications, cloud concepts, and solution architecture fundamentals. It is best suited for learners who want to deepen their architectural judgment, design decision-making, and enterprise readiness for agentic AI solutions—rather than those seeking step-by-step configuration guidance or exam preparation. Important Note: While this course aligns conceptually with many of the AB‑100 exam skill areas, it is not a test-preparation course and does not focus on test-taking strategies. Instead, it provides the architectural foundations, enterprise context, and design reasoning that make AB‑100 learning meaningful and applicable. For many learners, this course serves as a recommended preparatory step before beginning focused AB‑100 exam study. The ideal audience includes:
Solution Architects and Enterprise Architects designing intelligent and agent-based business solutions Senior Functional and Technical Consultants working with Dynamics 365, Microsoft 365, Power Platform, or Azure AI services AI and Digital Transformation Leads defining AI strategy, governance, and adoption across the organization Application Architects and Technical Leads integrating agents, copilots, and generative AI into enterprise workloads Experienced practitioners preparing to advance toward formal AI solution validation, seeking architectural depth rather than exam-focused instruction
Certifications
This course is part of the following Certifications:
Course Content
Introduction to agentic AI business solutions
- Introduction
- Drive AI transformation with architect strategies
- Explore Microsoft AI technologies for business
- Identify Microsoft AI technologies for business solutions
- Identify out-of-box Microsoft AI agent resources for business solutions
- Identify out-of-box Microsoft AI agents for business
- Module assessment
- Summary
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, and availability
- Organize business solution data for AI systems
- Module assessment
- Summary
Design overall AI strategy for business solutions
- Introduction
- Implement AI adoption process with Azure
- Design AI agents for business solutions
- Design a multi-agent solution
- Develop use cases for prebuilt Microsoft 365 Copilot agents
- Define solution rules and constraints for AI components
- Determine generative AI knowledge sources for agents built in Copilot Studio
- Determine when to build custom agents or extend Microsoft 365 Copilot
- Determine when custom AI models should be created
- Provide guidelines for creating a prompt library
- Develop use cases for customized small language models
- Provide prompt engineering guidelines and techniques
- Identify key business user roles for AI workloads
- Evaluate regional and local AI data regulation compliance requirements
- Include elements in a Microsoft AI Center of Excellence
- Design AI solutions using multiple Dynamics 365 apps
- Design user prompt training for AI solution adoption
- Module assessment
- Summary
Evaluate costs and benefits of AI solutions
- Introduction
- Evaluate ROI criteria for AI-powered solutions
- Create ROI analysis for a proposed AI solution
- Analyze whether to build, buy, or extend AI components
- Implement a model router to intelligently route requests to the most suitable model
- Module assessment
- Summary
Design AI agents for business solutions
- Introduction
- Define core tenets of responsible AI guidelines for AI business solutions
- Design business terms for Copilot in Dynamics 365 Customer Service
- Design customizations for Copilot in Dynamics 365 apps
- Design connectors for Copilot in Dynamics 365 Sales
- Design AI agents for Dynamics 365 Contact Center
- Design task agents in Microsoft Copilot Studio
- Design autonomous agents in Copilot Studio
- Design prompt-driven agents using Copilot Studio
- Propose Foundry tools given a requirement
- Propose code first generative pages and agent feed applications
- Design topics for Copilot Studio, including fallback
- Design data processing workflows for grounded AI
- Design business processes with AI in Power Apps canvas apps
- Apply the Microsoft Power Platform Well-Architected Framework to intelligent application workloads
- Determine the use of standard natural language processing
- Design agents and agent flows with Copilot Studio
- Design prompt actions in Copilot Studio
- Define success criteria and adoption goals for AI business solutions
- Module assessment
- Summarize AI agent design for business solutions
Design extensibility of AI solutions
- Introduction
- Design AI solutions with custom models in Microsoft Foundry
- Design agents in Microsoft 365 Copilot
- Design extensible agents in Microsoft Copilot Studio
- Design extensible agents using MCP in Copilot Studio
- Design agents to automate tasks in apps and websites with Computer Use in Copilot Studio
- Design agent behaviors in Copilot Studio
- Optimize solution design for agents in Microsoft 365
- Module assessment
- Summary
Orchestrate configuration of prebuilt agents and apps
- Introduction
- Design AI solutions for Dynamics 365 Customer Service
- Propose Microsoft 365 agents for business scenarios
- Orchestrate and configure Microsoft 365 Copilot for sales and service
- Propose Microsoft Power Platform AI features
- Design interoperable agent experiences for Finance and Operations
- Recommend process knowledge sources for in-app help in Dynamics 365
- Orchestrate AI features in Dynamics 365 Finance and Supply Chain
- Module assessment
- Summary
Monitor, analyze, and tune AI agents
- Introduction
- Recommend process tools for monitoring agents
- Analyze backlog and user feedback for AI agent usage
- Apply AI-based tools to analyze, identify issues, and perform tuning
- Monitor AI agent performance metrics
- Interpret telemetry data to tune AI performance
- Module assessment
- Ensure reliable AI agent operations
Manage testing AI-powered business solutions
- Introduction
- Recommend process metrics for testing AI agents
- Create validation criteria for custom AI models
- Validate effective Copilot prompt best practices
- Design end-to-end test scenarios for AI solutions using multiple Dynamics 365 apps
- Build a strategy for creating test cases using Copilot
- Module assessment
- Summary
Design ALM process for AI-powered business solutions
- Introduction
- Design an ALM process for data used in AI models and agents
- Design an ALM process for Copilot Studio agents, connectors, and actions
- Design ALM processes for Microsoft Foundry agents
- Design an ALM process for custom AI models
- Design an ALM process for AI in Dynamics 365 Finance and Supply Chain
- Design ALM processes for AI in Dynamics 365 apps
- Module assessment
- Summary
Design responsible AI security, governance, risk management, and compliance
- Introduction
- Design security agents for Microsoft clouds
- Design governance models for AI agents
- Design model security for responsible AI
- Analyze AI vulnerabilities and mitigations for prompt manipulation
- Review solution adherence to Responsible AI principles
- Validate data residency and movement compliance
- Design access controls for grounding data and model tuning
- Design audit trails for changes to models and data
- Module assessment
- Summary
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