Microsoft Agentic AI Business Solutions Architect Quick Facts (2025)

Exam Domain Breakdown

Domain 1: Plan AI-powered business solutions (28.34% of the exam)

Analyze requirements for AI-powered business solutions

• Assess the use of agents in task automation, data analytics, and decision-making.
• Review data for grounding, including accuracy, relevance, timeliness, cleanliness, and availability.
• Organize business solution data to be available for other AI systems.

Summary:
This section focuses on the foundational planning mindset for AI-driven business transformation. You will explore how to assess organizational needs, pinpoint automation opportunities, and evaluate data readiness. The emphasis is on ensuring that each decision made during early design phases directly supports accurate grounding and data-driven insight generation.

You will also learn how to evaluate and structure information so it becomes shareable and actionable across multiple AI systems and agents. By identifying the right data sources and ensuring integrity, you set the stage for scalable AI initiatives that align with business demands and deliver lasting value.

Design overall AI strategy for business solutions

• Implement the AI adoption process from the Cloud Adoption Framework for Azure.
• Design the strategy for building AI and agents in business solutions.
• Design a multi-agent solution using platforms such as Microsoft 365 Copilot, Copilot Studio, and Azure AI Foundry.
• Develop use cases for prebuilt agents in the solution.
• Define solution rules and constraints when building AI components with Copilot Studio, Azure AI services, and Azure AI Foundry.
• Determine the use of generative AI and knowledge sources 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 for the solution.
• Provide prompt engineering guidelines and techniques for AI-powered business solutions.
• Include the elements of the Microsoft AI Center of Excellence.
• Design AI solutions that use multiple Dynamics 365 apps.

Summary:
This section explores strategic design of intelligent architectures across Microsoft’s agent platforms. You will learn how to craft a holistic adoption plan that blends prebuilt and custom AI capabilities, aligning them under one cohesive AI strategy rooted in the Cloud Adoption Framework.

Expect to design systematic approaches for multi-agent integration, determine when to adapt existing Copilot capabilities, and build prompt libraries that reinforce consistent results. You will also integrate prompt engineering principles and small language model strategies to meet specific enterprise requirements, ensuring every design aligns with responsible and scalable AI practices.

Evaluate the costs and benefits of an AI-powered business solution

• Select ROI criteria for AI-powered business solutions, including total cost of ownership.
• Create ROI analyses for proposed AI solutions in business processes.
• 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.

Summary:
This section guides you in quantifying business value generated through AI investments. You will learn to measure potential returns and build robust financial cases by balancing total cost of ownership, speed of innovation, and scalability.

The focus extends to evaluating sourcing strategies for AI components—whether to build, buy, or extend existing assets—and implementing intelligent model routing. By combining financial insight with technical evaluation, you’ll be prepared to recommend the most strategic path to business value realization.

Domain 2: Design AI-powered business solutions (28.33% of the exam)

Design AI and agents for 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 Microsoft AI services for specific requirements.
• Propose code-first generative pages and use agent feeds in apps.
• Design topics for Copilot Studio, including fallback handling.
• Design data processing for AI models and grounding.
• Design business processes to include AI components in Power Apps canvas apps.
• Apply the Microsoft Power Platform Well-Architected Framework to intelligent workloads.
• Determine when to use natural language processing, conversational understanding, or generative AI orchestration in Copilot Studio.
• Design agents and agent flows in Copilot Studio.
• Design prompt actions in Copilot Studio.

Summary:
This section centers on creating intelligent business systems that connect people, processes, and data through agent design. You will gain a deep understanding of AI and agent architecture within Dynamics 365 and Copilot Studio to enhance app intelligence and user interaction.

You will focus on composable solution design—building agents that integrate across Dynamics, Sales, and Power Platform applications. Key learning outcomes include grounding data design, coordinating prompt actions, and maintaining alignment with Microsoft's best practices for well-architected AI-powered workloads.

Design extensibility of AI solutions

• Design AI solutions using custom models in Azure AI Foundry.
• Design agents in Microsoft 365 Copilot.
• Design extensibility for agents in Copilot Studio.
• Extend agents with Model Context Protocol in Copilot Studio.
• Design agents to automate tasks in apps and websites using Computer Use in Copilot Studio.
• Design agent behaviors, including reasoning and voice mode.
• Optimize solutions using agents in Microsoft 365, including Teams and SharePoint.

Summary:
This section emphasizes extending Microsoft’s AI ecosystem to deliver tailored business solutions. You will discover how to integrate custom models through Azure AI Foundry and extend Copilot experiences to meet enterprise-specific goals.

Practical expertise includes enabling automation through Computer Use, refining agent behavior through reasoning design, and ensuring seamless team collaboration. The goal is to optimize the reach and performance of AI agents across environments like Microsoft 365, Teams, and SharePoint.

Orchestrate configuration for prebuilt agents and apps

• Orchestrate AI in Dynamics 365 apps for finance, supply chain, customer experience, and service.
• Propose Microsoft 365 agents for key business scenarios.
• Orchestrate configuration of Microsoft 365 Copilot for Sales and Service.
• Propose Microsoft Power Platform AI features, including AI hub.
• Design interoperability of agent chats for finance and operations with additional knowledge sources.
• Recommend the process of adding knowledge sources to in-app help and guidance in Dynamics 365 Finance or Supply Chain Management.

Summary:
Here, the focus is on orchestration mastery—how multiple prebuilt agents and applications can work together cohesively. You will learn to connect Microsoft 365, Dynamics 365, and Power Platform components to achieve intelligent end-to-end workflows.

The section also equips you to configure cross-application orchestration, connecting diverse agents and adding business-context knowledge sources. By doing so, you ensure that enterprise users experience AI assistance that is contextual, integrated, and productive across all business layers.

Domain 3: Deploy AI-powered business solutions (43.33% of the exam)

Analyze, monitor, and tune AI-powered business solutions

• Recommend the process and tools required for monitoring agents.
• Analyze backlog and user feedback of AI and agent usage.
• Apply AI-driven analytics tools to identify issues and perform tuning.
• Monitor agent performance and operational metrics.
• Interpret telemetry data for performance and model tuning.

Summary:
This section builds your skills in maintaining excellence after deployment. You will explore methods to collect and interpret telemetry, evaluate key performance metrics, and identify tuning opportunities across complex agent and model ecosystems.

By leveraging Azure and Microsoft analytics tools, you will apply continuous learning cycles to improve both accuracy and user satisfaction. The aim is to ensure that deployed solutions remain adaptive, efficient, and strategically aligned with evolving business objectives.

Manage the testing of AI-powered business solutions

• Recommend processes and metrics to test agents.
• Create validation criteria for custom AI models.
• Validate effective Copilot prompt design practices.
• Design end-to-end test scenarios utilizing multiple Dynamics 365 applications.
• Build testing strategies incorporating Copilot-driven test cases.

Summary:
This section focuses on quality assurance practices for AI and agent testing. You will learn structured approaches to building repeatable, measurable test processes that validate model accuracy and user outcomes.

You will develop confidence in verifying that AI behaviors match intended results, ensuring reliable solutions when applied across Dynamics and Microsoft 365 environments. These testing strategies help maintain consistent quality and compliance as intelligent systems evolve.

Design the ALM process for AI-powered business solutions

• Design ALM processes for data used in AI models and agents.
• Design ALM processes for Copilot Studio agents, connectors, and actions.
• Design ALM processes for Azure AI service agents.
• Design ALM processes for custom AI models.
• Design ALM processes for Dynamics 365 AI in finance, supply chain, customer experience, and service apps.

Summary:
This section dives into the application lifecycle management (ALM) principles specific to AI components. You will learn frameworks for CI/CD pipelines, environment governance, and artifact management across diverse AI workloads.

Ensuring that data, agents, and custom models are versioned and traceable helps sustain quality across the solution lifecycle. This knowledge empowers teams to manage complexity confidently and roll out AI capabilities with precision and accountability.

Design responsible AI, security, governance, risk management, and compliance

• Design security for agents.
• Design governance for agents.
• Design model security.
• Analyze solution and AI vulnerabilities and mitigations, including prompt manipulation.
• Review solution alignment with 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.

Summary:
This section highlights Microsoft’s Responsible AI framework and governance best practices. You will learn how to ensure security and compliance without compromising flexibility and innovation, establishing trust in every system interaction.

By designing risk-aware governance models and building audit-ready data flows, you will uphold ethical AI principles while maintaining model performance and control. This knowledge helps safeguard data, sustain compliance, and promote responsible adoption of agentic AI across the organization.