Agentic AI Strategy & Solution Planning

Microsoft via Coursera

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Overview

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Agentic AI is reshaping how enterprises automate decisions, retrieve knowledge, and orchestrate complex workflows. This course gives you the strategic and architectural foundation to lead that transformation using Microsoft's AI platform. You'll explore core agentic AI concepts, map the full Microsoft ecosystem—including Copilot Studio, Microsoft Foundry, Microsoft 365 Copilot, Dynamics 365, and Power Platform—and apply structured frameworks to determine when to build, buy, or extend AI solutions. You'll assess enterprise data readiness, identify high-value agent use cases, and design adoption strategies grounded in Microsoft's Cloud Adoption Framework. By the end of this course, you'll be able to conduct enterprise requirements analysis, recommend the right platform components for a given scenario, design a high-level multi-agent strategy, and build a defensible ROI case for an agentic AI initiative. This course is ideal for solutions consultants, AI architects, and technical leads preparing to take on agentic AI architecture responsibilities. Foundational familiarity with Microsoft cloud platforms and business process design is recommended.

Syllabus

Agentic AI Fundamentals: Articulate Agentic AI Concepts & Map the Microsoft Ecosystem
- This module establishes the conceptual and platform foundation for the entire program. You will establish the architectural boundaries between deterministic automation and non-deterministic agentic systems, and see how the different layers of the Microsoft AI platform ecosystem—agent reasoning, memory, and tool integration—fit together to enable agentic solutions at enterprise scale.
Agentic AI Fundamentals: Design Model Routers & Evaluate Build/Buy/Extend Trade-offs
- This module develops architectural judgment for two of the most consequential early decisions in an agentic AI engagement: which model or models to use, and whether to build, buy, or extend an agent solution. You will design model routers, evaluate SLM use cases, and apply structured decision frameworks to real enterprise scenarios.
Agent Opportunity Analysis: Assess Where Agents Deliver Value
- This module develops the diagnostic skills needed at the start of an agentic AI engagement, identifying and prioritizing high-value agent use cases across enterprise workflow types before any data-readiness or platform decisions are made. You'll apply a value assessment framework to a realistic retail scenario and practice categorizing and prioritizing use cases the way a practicing architect would in an early-stage client conversation.
Agent Opportunity Analysis: Evaluate Data Grounding Readiness
- This module develops the conceptual and procedural foundation needed to evaluate data readiness, understanding the four core data readiness concepts, connecting them to personal experience through a reflection activity, and applying a four-step audit procedure to a realistic healthcare scenario.
Enterprise AI Strategy: Apply the Cloud Adoption Framework for AI
- This module develops the strategic planning and execution skills needed to structure an enterprise AI transformation using the Cloud Adoption Framework for Azure. Learners understand why unstructured AI adoption fails, map the four critical components to the correct CAF phases, and apply a four-step execution playbook to a realistic finance department scenario, building the architectural judgment needed to lead a controlled AI rollout from intake to production.
Enterprise AI Strategy: Design an AI Center of Excellence & Multi-Platform Agent Strategy
- This module translates the CAF adoption framework into an actionable enterprise agent strategy. Learners understand why governance structure is an architectural decision, map the precise platform architecture and accountability matrix of a governed Microsoft AI ecosystem, and apply a four-phase implementation blueprint to a realistic HR department scenario, building the end-to-end design judgment needed to deploy and govern a multi-platform agent strategy in practice.
Project Module: Agentic AI Strategy & Solution Planning
- Learners step into the role of Principal AI Strategy Consultant and Lead Architect for Apex Premium Finance—a mid-sized wealth management and retail banking institution facing uncoordinated AI requests from three business units. Rather than producing a generic strategy document, learners apply every LC 1 skill to a realistic enterprise scenario with real stakes: preventing shadow AI chaos, classifying project risk, remediating data gaps, selecting the right platform for each use case, designing a phased adoption roadmap, and establishing a Center of Excellence accountability model. The result is a portfolio-ready Enterprise AI Strategy and Governance Playbook that mirrors the deliverables a practicing AI Solutions Architect produces in a client engagement.