hubsp-ai-skill

What is this project

This repository organizes a practical knowledge base to guide the corporate adoption of AI in the Microsoft ecosystem. The focus is on selecting the simplest viable architecture first, then combining Microsoft IQ context workloads (Work IQ, Foundry IQ, Fabric IQ and Web IQ), explicit agent runtimes, governed data, security, guard rails, evaluators and operations only where the current requirements justify them.

The project was structured to serve as both an executive and technical reference: it helps business, architecture, data and security teams align on a common vision for planning, piloting and expanding AI initiatives with lower risk and greater value predictability. Skill version 1.10.0 accepts either the completed 30-question intake or any equivalent business and technical scenario document, makes low-complexity-first selection mandatory, requires evidence before escalating to agentic, multi-agent or mixed-runtime solutions, and requires a capacity and scale fit verdict before a Microsoft product, SaaS capability, license, SKU or configuration is approved for production. The skill metadata (name and description) remains compatible with Claude’s custom-skill upload requirements.

Disclaimer

This is an independent community project. It is not an official Microsoft repository and is not endorsed, maintained or supported by Microsoft.

All outputs and recommendations produced by this skill must be reviewed and validated through a Human-in-the-Loop (HITL) process by qualified stakeholders before they are used for business, architecture, security, compliance or production decisions.

Objective

• Define a reference architecture for AI agents in a corporate context.
• Evaluate existing products, deterministic automation, LLM, RAG, single-agent and multi-agent options in ascending complexity.
• Stop at the first option that reliably satisfies current requirements and require evidence before every escalation.
• Validate product licensing, quotas, throughput, concurrency, data/index size, regional availability, downstream limits and scale headroom against peak requirements.
• Require component-level technical justification and traceable official links for every element in the target architecture, with documented capabilities separated from assumptions, design decisions and measured evidence.
• Standardize recommendations focused on governance, compliance and observability.
• Support a wave-based roadmap (pilot, expansion and scale) with clear criteria.
• Make trade-offs explicit across cost, risk, speed and complexity.

Low-complexity-first decision policy

The skill evaluates options in this mandatory order:

flowchart LR
   A[Existing productor SaaS] --> B[Deterministic automationrules, APIs, Power Automate]
   B --> C[Direct LLMone-step generation]
   C --> D[RAGgrounded response]
   D --> E[Single AI Agentbounded tool use]