ChrisMcKee1

Microsoft Foundry RBAC: What Changed, and What You Should Do

A practical guide to the new Microsoft Foundry (the "new" portal at ai.azure.com) role model. It covers the role rename, which roles to stop using, which to start using, and exactly who can deploy models.

Every statement below is sourced to official Microsoft Learn documentation. Where two Microsoft pages disagree, that disagreement is called out explicitly.

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TL;DR

ChrisMcKee1

SharePoint App-Only Access with Certificate Auth & Sites.Selected

A complete, phased walkthrough for building service-to-service SharePoint access using certificate-based client credentials and the least-privilege Sites.Selected permission model.

Follow every phase in order. Each phase ends with a milestone check — do not proceed until you pass it. A handoff table shows exactly which values carry forward into the next phase so nothing gets lost.

ChrisMcKee1

SharePoint grounding connection (AI Foundry) — learning guide

This guide shows the exact az rest calls to create, list, and delete an AI Foundry SharePoint grounding connection.

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Mental model (two APIs)

You’ll touch two different API surfaces:

ChrisMcKee1

Salesforce Agentforce and Azure AI Foundry Agent Integration

Overview

This document explores integration patterns between Salesforce Agentforce and Azure AI Foundry Agent Service using the Microsoft Agent Framework and Agent-to-Agent (A2A) protocol.

Research Date: November 2025
Key Technologies:

• Microsoft Agent Framework (Released October 2025)
• Azure AI Foundry Agent Service (GA May 2025)

ChrisMcKee1

Azure RBAC Roles for Non-Privileged Developer Access

Purpose: Enable developers to develop and debug locally using non-privileged accounts with appropriate read/write permissions to Azure resources, following the principle of least privilege.

Key Principle: Use separate role assignments for Development (user identities) vs. Production (service principals/managed identities via CI/CD).

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Table of Contents

ChrisMcKee1

Microsoft Agent SDKs: Strategic Comparison & Business Value Propositions

Author: Business Analysis & Technical Decision Framework
Date: January 2025
Version: 1.0
Purpose: Executive and technical decision-making guide for choosing among Microsoft's agent SDK ecosystem

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Executive Summary

ChrisMcKee1

Azure Functions HTTP Trigger Authentication with AI Foundry Agents SDK

Complete Guide Based on Microsoft Official Documentation

Overview

This guide explains how to implement authentication for HTTP-triggered Azure Functions when integrating with the Azure AI Foundry Agents SDK. It covers:

• Function endpoint authentication (App Service Easy Auth with Microsoft Entra ID)
• Outbound authentication from functions to Azure resources (Managed Identity)
• OpenAPI specification integration for agent discovery

ChrisMcKee1

Oil & Gas: Predictive Maintenance and Safety Management Solution Overview

1. Introduction: Project Goals and Scope

This solution presents an end-to-end, intelligent architecture built on Microsoft technologies like Azure and Power Platform, designed to transform operations in the Oil & Gas industry by integrating real-time data, AI, and automation. It addresses three of the sector’s most pressing challenges: unplanned equipment downtime , worker safety in high-risk environments and Operational Efficiency.

Project Goals

• Reduce Operational Costs & Downtime: Implement AI-powered predictive maintenance to anticipate equipment failures, minimizing unplanned downtime and associated costs.
• Enhance Safety Management: Utilize AI to monitor hazardous processes, analyze sensor data, and predict potential safety incidents, thereby improving overall safety.

ChrisMcKee1

🧭 Bridging the Chasm: A Framework for Managing Al-Infused Application Development in the Enterprise

🚀 1. Introduction: The New Frontier of Al-Infused Application Development

❓ Framing the Challenge: Merging Exploratory Al with Predictable Delivery

The core challenge lies in the fundamental differences between traditional software development and Al development paradigms[^4]. Software teams, often operating under Agile or Waterfall methodologies, rely on well-defined requirements, predictable lifecycles, and measurable progress towards shippable increments[^5]. Conversely, Al and data science initiatives, even those focused on leveraging existing LLMs, involve inherent uncertainty, experimentation, and iteration[^6]. Data scientists and Machine Learning (ML) engineers explore possibilities, refine approaches based on empirical results, and often produce research papers or prototypes as primary outputs, contrasting sharply with the software world's focus on