EP02: Secret Microsoft 365 AI Architecture Blueprint for Power Platform Builders

Описание: Start building reusable Microsoft 365 AI architecture that scales—common services + business apps + UI patterns for Power Platform builders. Microsoft 365 AI architecture

Who this is for: Developers, architects, and Power Platform builders focused on Microsoft 365 and Azure AI. If you design AI solutions, integrations with Azure OpenAI / Foundry, or enterprise automation—this video shows the proven blueprint we use in production.

What you’ll learn:
How to structure a three-layer architecture: Common Services, Business Logic, UI
Why ‘‘do not repeat yourself’’ matters for Azure OpenAI calls, batching, and token cost
How to centralize AI model selection and avoid breaking changes across apps
Patterns for chat history, message arrays, prompt catalogs, and vector DB queries
When to escalate to Azure (web sockets, real-time API) for voice/streaming agents
How to implement OCR & Document Intelligence vs Visual AI responsibilities
How to package reusable flows & utilities using Power Automate Solutions
Practical steps to cut new solution ship time by ~60% using this framework

Free Assessment: [Free Assessment Link]

Series: Power Agents Series • Ep02 (Microsoft 365 AI build series, 2026)

Chapters:
0:00 - Who this architecture is for
0:33 - Mastermind-tested framework overview
0:55 - Whiteboard: three-layer architecture intro
1:13 - Repeatable patterns and the common core
2:06 - Why Power Platform solutions matter
2:49 - Single-tenant environments and virtual segmentation
4:02 - Centralizing AI HTTP requests and parameters
4:35 - Business scenarios: chat history, memory, live data
5:22 - Batching, large JSONL, cost optimization
7:00 - Edge case: real-time streaming and web sockets
8:01 - Deep dive: Common Services responsibilities
9:36 - Document Intelligence (OCR) vs Visual AI needs
12:09 - Business layer: solutions and reuse patterns
14:06 - Contract-driven change management (inputs/outputs)
16:20 - Message arrays, system prompts, and memory
20:59 - Utilities to reduce engineering work
21:53 - Start new apps with 60% prebuilt components
22:55 - UI layer: APIs, Copilot, Power Apps, and UIs
26:36 - Multi-channel progression: YouTube → LinkedIn → TikTok
27:23 - Roadmap: building the common services next
28:15 - Call for use cases and next steps

Key takeaways / Resources:
Build a single Common Services solution (Power Automate Solution) that encapsulates AI HTTP interaction, model mapping, prompt catalog, chat history, vector queries, OCR/document intelligence utilities, and batching helpers.
Keep contract-stability: inputs and outputs must remain stable while the common service can swap AI models and optimize internals.
Use Power Platform environments inside a single Microsoft 365 tenant for dev/qa/prod segmentation; leverage solution export/import for deployments.
Treat real-time streaming as an edge case: escalate to Azure Functions + WebSocket + Event Grid for voice/streaming agents.
Prioritize batching + JSONL for high-volume document processing to dramatically reduce token cost and runtime spikes.

Implementation hints:
Map business requests to a small set of capability levels (e.g., "think", "standard", "many", "nano") and let common services decide which model/version to call.
Build utilities that return predictable JSON contracts: { status, data, errors, meta } so business apps can fail gracefully.
Provide a prompt catalog service (versioned) and a vector retrieval wrapper so business flows only need to supply the query and context parameters.


Microsoft 365 AI architecture,Power Platform architecture,Azure OpenAI Foundry,Power Automate solutions,AI common services,Prompt catalog,Chat history patterns,Document intelligence OCR,Visual AI integration,Batching JSONL costs,Real-time AI streaming,Azure Functions WebSocket,Enterprise AI design,Copilot integration,SharePoint AI builders,Power Apps AI patterns,Vector DB queries,AI model management

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