Architecting Agentic AI Business Solutions (AB-100)
Certification Preparation for the "Agentic AI Business Solutions Architect"
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Machine Learning Engineer
Learn how to develop AI-powered business solutions that leverage agents, copilots, and generative AI to automate tasks, improve decision-making, and customers the experiences of employees and customers . The focus is on architecture, trade-offs, governance, cost-benefit analysis, and lifecycle management—not on step-by-step configuration.
1. Introduction to Agent-Based AI Business Solutions
Learn how to align AI solutions with your business goals and scale your AI implementation using Microsoft technologies.
2. Analyze requirements for AI-powered business solutions
Learn how to analyze, design, and implement AI-powered business solutions using agents, generative AI, and Microsoft Copilot for Productivity.
3. Evaluate the costs and benefits of AI solutions
Learn how to evaluate the costs, benefits, and ROI of AI solutions while deciding whether to develop, purchase, or extend AI components for your business processes.
4. Manage the testing of AI-powered business solutions
Learn how to validate and maintain AI-powered business solutions using structured testing frameworks, metrics, and governance.
5. Scalability of AI solutions
Learn how to develop scalable, secure, and customizable AI solutions using Microsoft platforms.
6. Designing Responsible AI Security, Governance, Risk Management, and Compliance
Learn how to design secure, regulated, and compliant AI systems that align with your company’s policies and the principles of responsible AI.
Additional information:
Important Note: While this course covers many of the same topics as the AB-100 exam, it is not a test preparation course and does not focus on exam strategies. Instead, it teaches the architectural fundamentals, the business context, and the design considerations that make studying for the AB-100 exam meaningful and practical. For many Learners , this course Learners as recommended preparation before they begin focused study for the AB-100 exam.
- Promoting the adoption of AI by defining the role of architects and aligning solutions with business objectives
- Assessing data quality (accuracy, relevance) in terms of AI readiness and scalable use
- Defining ROI criteria, total cost of ownership (TCO), and strategic decisions regarding development, purchase, or expansion
- Implement structured testing processes and validation criteria for performance and compliance
- Securing AI agents through identity management, data governance, and threat protection
- Exploring the business benefits of Copilot/generative AI for boosting corporate productivity
- Applying model routing strategies to scale AI and optimize performance and costs
- Extending agent capabilities using Copilot Studio and the Model Context Protocol (MCP)
- Assess where AI agents provide measurable value in task automation and decision-making
This course consists of training training and is led by a trainer who supervises the participants live. Theory and practice are taught with live demonstrations and practical exercises. The video conferencing software Zoom is used.
This course is designed for experienced technology professionals who are responsible for planning, designing, and managing AI-powered business solutions on Microsoft platforms. This course requires knowledge of Microsoft business applications, cloud concepts, and the fundamentals of solution architecture. It is best suited for Learners who want to deepen their architectural judgment, design decisions, and organizational readiness for agent-based AI solutions—and is not intended for those seeking step-by-step configuration instructions or exam preparation.
The ideal target audience includes:
- Solution architects and enterprise architects who design intelligent, agent-based business solutions
- Experienced functional and technical consultants who work with Dynamics 365, Microsoft 365, Power Platform, or Azure AI services
- AI and digital transformation leaders who define AI strategy, governance, and implementation across the entire organization
- Application architects and technical leads who integrate agents, copilots, and generative AI into enterprise workloads
- Experienced practitioners who are preparing for the formal validation of AI solutions and are looking for architectural depth rather than exam-oriented instructions
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