Course AB-100T00 Architecting agentic AI business solutions
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.
After completing this course, students will be able to:
1. AI Strategy and Scaling
- Align AI solutions with business goals and scale AI adoption across the enterprise using Microsoft technologies.
- Design a comprehensive enterprise AI strategy using Azure's Cloud Adoption Framework, mapping the AI agent lifecycle to operational excellence.
- Assess costs, benefits, and ROI of AI investments to strategically decide whether to build, buy, or extend AI components.
2. Solution Design and Implementation
- Analyze, design, and deploy AI-powered business solutions utilizing autonomous agents, generative AI, and Microsoft Copilot.
- Develop tailored AI agents specific to organizational needs within Dynamics 365 and Copilot.
- Orchestrate, configure, and extend intelligent experiences across both Dynamics 365 and Microsoft 365 Copilot ecosystems.
- Architect scalable, secure, and highly customizable AI solutions built on Microsoft platforms.
3. Governance, Testing and Lifecycle Management
- Ensure high-quality, reliable AI operations that deliver consistent outcomes and continuously adapt to enterprise needs.
- Validate and maintain AI systems using structured testing frameworks, performance metrics, and strict governance protocols.
- Establish robust ALM processes for AI solutions to ensure security, compliance, and consistency across environments.
- Design secure and compliant AI architectures that rigorously align with corporate policies and Responsible AI principles.
Audience Profile
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. 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
Before attending this course, students must have:
1. AI and Machine Learning
- Basic understanding of AI and machine learning concepts.
- Familiarity with AI concepts and business processes
- Basic understanding of AI agent lifecycle and governance.
- Familiarity with AI agent workflows and enterprise environments.
- Design ALM processes for AI components like datasets, prompts, and models.
- Ensure consistent and compliant AI behavior from development to production.
2. Microsoft 365, Dynamics 365 and Power Platform
- Familiarity with Microsoft 365 tools and enterprise data management.
- Basic understanding of Microsoft 365 and Dynamics 365 platforms
- Familiarity with Dynamics 365 and AI concepts.
- Basic understanding of Power Platform and Dataverse.
- Working knowledge of Microsoft Entra ID, Azure RBAC, Azure monitoring, data governance concepts, and Copilot Studio administration.
3. Architecture and Cloud
- Experience in solution architecture or enterprise system design
- Familiarity with AI concepts and enterprise architecture.
- Familiarity with Azure Cloud Adoption Framework.
4. Business and Governance
- Basic understanding of ROI and TCO principles.
- Basic understanding of monitoring tools and governance standards.
- Establish governance, security, and monitoring across environments.
1. Introduction to agentic AI business solutions
- 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
2. Analyze requirements for AI-powered business solutions
- 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
3. Design overall AI strategy for business solutions
- 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
4. Evaluate costs and benefits of AI solutions
- 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
5. Design AI agents for business solutions
- 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
6. Design extensibility of AI 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
7. Orchestrate configuration of prebuilt agents and apps
- 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
8. Monitor, analyze, and tune AI agents
- 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
9. Manage testing AI-powered business solutions
- 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
10. Design ALM process for AI-powered business solutions
- 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
11. Design responsible AI security, governance, risk management, and compliance
- 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
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