Architecting agentic AI business solutions (AB-100T00-A)
Contact us to book this course
Delivery methods
On-Site, Virtual
Duration
3 days
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 Objectives
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Design end-to-end agentic AI solutions aligned with business goals
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Analyze requirements and identify appropriate AI, Copilot, and agent use cases
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Evaluate cost, ROI, and build vs. buy decisions for AI solutions
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Architect scalable and extensible solutions using Microsoft Copilot and Power Platform • Implement robust testing strategies and validation frameworks for AI systems
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Apply responsible AI principles including security, governance, and compliance
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
Prerequisites
- Basic understanding of AI concepts
- Familiarity with business process analysis
- Knowledge of Microsoft tools and platforms
Course outline
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
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
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
Review data for grounding accuracy, relevance, timeliness, cleanliness, and availability
Organize business solution data for AI systems
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
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
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
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
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
Summarize AI agent design for 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
Summarize AI agent design for business solutions
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
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
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
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
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
Ensure reliable AI agent operations
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
Ensure reliable AI agent operations
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
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
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
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
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
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
