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Microsoft Certified: Agentic AI Business Solutions Architect Study Guide
Phase 1: First Principles of Agentic AI Architecture1.1.What Makes AI "Agentic"1.1.1.The Agent Spectrum: From Copilots to Autonomous Agents1.1.2.How Agents Perceive, Reason, and Act1.2.The Microsoft Agentic AI Ecosystem1.2.1.Copilot Studio, Microsoft Foundry, and Dynamics 3651.2.2.Open Standards: MCP and Agent2Agent (A2A)1.3.Architectural Foundations for AI Solutions1.3.1.Multi-Agent Orchestration Patterns1.3.2.Grounding, Knowledge Sources, and Data Flow1.4.Reflection CheckpointPhase 2: Planning AI-Powered Business Solutions (25–30%)2.1.Analyzing Requirements for AI Solutions2.1.1.Assessing Agent Use Cases for Automation, Analytics, and Decision-Making2.1.2.Evaluating Data Quality for Grounding2.1.3.Organizing Business Data for AI Consumption2.2.Designing an Overall AI Strategy2.2.1.The Cloud Adoption Framework AI Adoption Process2.2.2.Building an AI Center of Excellence2.2.3.Designing Multi-Agent Solutions Across Platforms2.2.4.When to Build, Extend, or Use Prebuilt Agents2.2.5.Custom AI Models and Small Language Models2.2.6.Prompt Libraries and Engineering Guidelines2.3.Evaluating Costs and Benefits2.3.1.ROI Criteria and Total Cost of Ownership2.3.2.Build, Buy, or Extend Decisions for AI Components2.3.3.Model Routing for Cost and Performance Optimization2.4.Reflection CheckpointPhase 3: Designing Agents with Copilot Studio (25–30%)3.1.Agent Types and Design Patterns3.1.1.Task Agents3.1.2.Autonomous Agents3.1.3.Prompt and Response Agents3.2.Building Agent Logic in Copilot Studio3.2.1.Designing Topics and Fallback Behavior3.2.2.Agent Flows and Orchestration3.2.3.Prompt Actions in Copilot Studio3.2.4.NLP vs. CLU vs. Generative AI Orchestration3.3.Extending Agent Capabilities3.3.1.Agent Extensibility with Model Context Protocol3.3.2.Computer Use for UI Automation3.3.3.Agent Behaviors: Reasoning and Voice Mode3.4.Data Processing for AI Models and Grounding3.5.Reflection CheckpointPhase 4: Designing AI for Dynamics 365 and Power Platform (25–30%)4.1.AI in Dynamics 365 Customer Experience and Service4.1.1.Business Terms and Copilot Customizations4.1.2.Agents for Contact Center Channels4.1.3.Connectors for Copilot in Dynamics 365 Sales4.2.AI in Dynamics 365 Finance and Supply Chain4.2.1.Orchestrating AI Features in Finance and Operations Apps4.2.2.Knowledge Sources for In-App Help and Guidance4.3.AI Solutions with Microsoft Foundry4.3.1.Custom Models in Microsoft Foundry4.3.2.Code-First Generative Pages and Agent Feeds4.4.Power Platform AI Integration4.4.1.AI Components in Power Apps Canvas Apps4.4.2.The Well-Architected Framework for Intelligent Workloads4.5.Orchestrating Prebuilt Agents and Microsoft 3654.5.1.Microsoft 365 Agents for Business Scenarios4.5.2.Copilot for Sales and Copilot for Service4.5.3.Agents in Microsoft 365 Copilot4.6.Reflection CheckpointPhase 5: Deploying, Monitoring, and Testing AI Solutions (40–45%)5.1.Monitoring Agent Performance5.1.1.Tools and Processes for Agent Monitoring5.1.2.Interpreting Telemetry Data for Tuning5.1.3.Analyzing Backlog and User Feedback5.2.Testing AI-Powered Solutions5.2.1.Testing Processes and Metrics for Agents5.2.2.Validation Criteria for Custom AI Models5.2.3.Validating Copilot Prompt Best Practices5.2.4.End-to-End Test Scenarios Across Dynamics 3655.2.5.Building Test Strategies with Copilot5.3.Reflection CheckpointPhase 6: ALM, Security, Governance, and Responsible AI (40–45%)6.1.Application Lifecycle Management for AI Solutions6.1.1.ALM for Copilot Studio Agents and Connectors6.1.2.ALM for Microsoft Foundry Agents and Custom Models6.1.3.ALM for AI in Dynamics 365 Apps6.2.Securing AI Solutions6.2.1.Agent Security and Governance6.2.2.Model Security and Prompt Manipulation Defense6.2.3.Access Controls on Grounding Data and Model Tuning6.3.Responsible AI, Compliance, and Risk Management6.3.1.Responsible AI Principles in Practice6.3.2.Data Residency and Movement Compliance6.3.3.Audit Trails for Models and Data6.4.Reflection CheckpointPhase 7: Exam Readiness7.1.Domain Weight Strategy and Study Priorities7.2.High-Frequency Traps and Decision Trees7.3.Scenario-Based Practice QuestionsPhase 8: GlossaryPhase 9: Conclusion
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7. Exam Readiness

Related sections

  • 1Phase 1: First Principles of Agentic AI Architecture
  • 2Phase 2: Planning AI-Powered Business Solutions (25–30%)
  • 3Phase 3: Designing Agents with Copilot Studio (25–30%)
  • 4Phase 4: Designing AI for Dynamics 365 and Power Platform (25–30%)
  • 5Phase 5: Deploying, Monitoring, and Testing AI Solutions (40–45%)
  • 6Phase 6: ALM, Security, Governance, and Responsible AI (40–45%)
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Alvin Varughese
Written byAlvin Varughese
Founder•18 professional certifications
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