An 11-module Microsoft Learn training path for solution architects: how to analyze requirements and design, cost, build, orchestrate, monitor, test, ship, and govern AI-powered business solutions using agents, generative AI, and Microsoft Copilot.
Each chapter here distils one module — its units condensed into the key concepts, paired with a quiz. These are independent study notes; take the free, hands-on modules on Microsoft Learn for the full path.
Original work & credit
Microsoft
Free, self-paced training path on Microsoft Learn for AI solution architects.
- Title
- Architect AI Solutions for Business Productivity
- Publisher
- Microsoft Learn
- Source
- Microsoft Learn training path
These notes are independent, original summaries created for study purposes. All rights to the underlying work belong to Microsoft and Microsoft Learn. Please support the authors by taking the original training.
Chapters
11 of manyIntroduction to Agentic AI Business Solutions
The AI architect's role in transformation, the Microsoft AI technology stack (Azure AI, Copilot, Copilot Studio), and out-of-box prebuilt agents for business.
Analyze Requirements for Agentic AI Solutions
Framing value areas, grounding agents in enterprise data, the five data-quality dimensions, and building an AI-ready data estate before you design.
Design an Overall AI Strategy
Mapping the Cloud Adoption Framework onto the agent lifecycle, single- vs multi-agent design and five orchestration patterns, the build-vs-extend matrix, and the AI Center of Excellence.
Evaluate Costs and Benefits of AI Solutions
TCO vs ROI, a seven-step ROI analysis with formulas and a worked email-triage example, the build/buy/extend decision matrix, and cost control with a model router.
Design AI Agents for Business Solutions
Responsible AI's six tenets, the three agent archetypes (task, autonomous, prompt-driven) and their six-part anatomy, agent flows and prompt actions, and success criteria & adoption goals.
Design Extensibility of AI Solutions
Custom models in Microsoft Foundry, the four extensibility layers, the Model Context Protocol (MCP) and Computer Use, and tuning reasoning depth, voice mode, and Microsoft 365 grounding.
Orchestrate Configuration of Prebuilt Agents and Apps
The three Dynamics 365 experience models (Sidecar/Embedded/Outside), agents-as-products with a readiness checklist and RACI, configuring Copilot for Sales & Service, and Power Platform AI via the AI Hub.
Monitor, Analyze, and Tune AI Agents
The five-dimension monitoring model and its tools (Azure Monitor, admin analytics, Power BI), the metric families, and a telemetry-to-tuning workflow that maps each signal to a root cause and fix.
Manage Testing AI-Powered Business Solutions
The testing framework and four test types, six-dimension validation criteria with real success thresholds, and end-to-end scenarios that span multiple Dynamics 365 apps.
Design an ALM Process for AI-Powered Business Solutions
Environment strategy with red/gold data, a seven-phase data ALM with promotion gates, ALM for Copilot Studio agents/connectors/actions, and MLOps discipline for custom models.
Design Responsible AI Security, Governance, Risk & Compliance
The eight governance domains and identity foundations, AI vulnerabilities (prompt manipulation) with layered defense, and immutable audit trails for model and data changes. Completes the 11-module path.