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:
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).
Complete Guide Based on Microsoft Official Documentation
This guide explains how to implement authentication for HTTP-triggered Azure Functions when integrating with the Azure AI Foundry Agents SDK. It covers:
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.
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