- Duration: 3 days
- Course Overview
This course prepares learners for the AB-100: Agentic AI Business Solutions Architect certification exam. It equips solution architects and AI professionals with the knowledge and practical skills required to design, plan, deploy, and govern AI-powered business solutions using Microsoft AI technologies.
Participants will gain hands-on exposure to architecting agentic AI solutions, orchestrating multi-agent systems, integrating Microsoft business applications, and ensuring responsible, secure, and scalable AI deployments.
- Target Audience
- Solution Architects
- AI / Data Architects
- Enterprise Architects
- Dynamics 365 & Power Platform Consultants
- Microsoft Copilot & Foundry Specialists
- Technical Leads driving AI transformation
- Learning Objectives
By the end of this program, participants will be able to:
- Architect AI-driven business solutions aligned to enterprise goals
- Design agentic-first and multi-agent orchestrated systems
- Integrate Microsoft Copilot, Dynamics 365, and Power Platform AI
- Implement secure, scalable, and governed AI environments
- Monitor, tune, and optimize AI agents
- Conduct ROI and cost-benefit analysis for AI initiatives
- Apply Responsible AI and compliance frameworks
- Course Modules
Module 1 — Introduction to Agentic AI Architecture
- Fundamentals of AI-powered business solutions
- Generative AI and enterprise transformation
- Overview of Microsoft AI ecosystem
- Agentic-first business processes
- Multi-agent orchestration concepts
- Foundry tools and models introduction
Module 2 — Planning AI-Powered Business Solutions (25–30%)
2.1 Requirements Analysis
- Identifying AI opportunities in business processes
- Agents for task automation and decision intelligence
- Data readiness assessment
- Accuracy
- Relevance
- Timeliness
- Cleanliness
- Availability
- Grounding data preparation
2.2 AI Strategy Design
- Cloud Adoption Framework for AI
- Enterprise AI roadmap development
- AI adoption lifecycle
- AI Center of Excellence framework
2.3 Agent Strategy & Use-Case Development
- Prebuilt vs custom agents
- Microsoft 365 Copilot extensibility
- Copilot Studio agent design strategy
- Foundry agent development planning
- Multi-agent architecture design
2.4 Generative AI & Model Decisions
- Custom vs prebuilt AI models
- Small language model use cases
- Model routing strategies
- Knowledge source selection
2.5 Prompt Engineering Framework
- Prompt library creation
- Prompt governance
- Enterprise prompt design standards
2.6 Business Value & ROI
- Total Cost of Ownership (TCO)
- ROI modeling for AI solutions
- Build vs Buy vs Extend analysis
Module 3 — Designing AI-Powered Business Solutions (25–30%)
3.1 Designing AI & Agents
- Task agents vs autonomous agents
- Prompt & response agents
- Dynamics 365 Copilot customization
- Copilot for Sales connectors
- Contact Center AI agents
3.2 Copilot Studio Solution Design
- Topic design & fallback strategies
- Agent flows & orchestration
- Prompt actions
- Conversational design patterns
3.3 Data & Grounding Architecture
- Knowledge ingestion pipelines
- Data processing for AI grounding
- Secure data connections
3.4 Power Platform AI Integration
- AI in Power Apps canvas apps
- Intelligent app workloads
- Well-Architected Framework application
3.5 AI Model & Foundry Design
- Custom models in Microsoft Foundry
- Code-first generative experiences
- Agent feed architecture
Module 4 — Agent Extensibility & Interoperability
- Agents in Microsoft 365 Copilot
- Extending Copilot Studio agents
- Model Context Protocol (MCP) integration
- Agent2Agent (A2A) interoperability
- Computer Use agents for UI automation
- Voice & reasoning agent behaviors
Module 5 — Orchestrating AI Across Business Applications
5.1 Dynamics 365 AI Orchestration
- Finance & Supply Chain AI features
- Customer Service AI orchestration
- Sales AI configuration
5.2 Microsoft 365 Copilot Ecosystem
- Copilot for Sales
- Copilot for Service
- Teams & SharePoint agents
5.3 Knowledge Integration
- External knowledge sources
- In-app guidance AI
- Finance & Operations chat extensibility
Module 6 — Deploying AI-Powered Business Solutions (40–45%)
6.1 Monitoring & Performance Optimization
- Agent telemetry monitoring
- Usage analytics
- Feedback loop systems
- Performance tuning techniques
6.2 Testing AI Solutions
- Agent validation frameworks
- Prompt testing methodologies
- Custom model validation
- End-to-end AI scenario testing
- Copilot-assisted test generation
Module 7 — Application Lifecycle Management (ALM)
- ALM for AI data pipelines
- Copilot Studio ALM
- Connector & action lifecycle
- Foundry agent ALM
- Custom model lifecycle governance
- Dynamics 365 AI ALM strategies
Module 8 — Responsible AI, Security & Governance
8.1 Responsible AI Framework
- Microsoft Responsible AI principles
- Ethical AI design
8.2 Security Architecture
- Agent security models
- Model protection strategies
- Data access controls
8.3 Risk & Compliance
- Prompt injection mitigation
- Vulnerability detection
- Data residency compliance
8.4 Governance & Auditability
- AI governance frameworks
- Audit trails for models & data
- Change tracking & monitoring
8.5 Practical Components
- Hands-on labs with Copilot Studio
- Multi-agent solution design workshop
- Dynamics 365 AI implementation lab
- Prompt engineering exercises
- AI monitoring dashboards setup
- ROI case study development
8.6 Assessment & Certification Preparation
- Module quizzes
- Architecture design assignments
- Mock exam simulations
- Case study presentations
8.7 Tools & Technologies Covered
- Microsoft 365 Copilot
- Copilot Studio
- Microsoft Foundry & Foundry Models
- Dynamics 365 AI
- Power Platform AI Hub
- Azure AI Services
- Model Context Protocol (MCP)
- Agent2Agent (A2A)