Browse Artificial Intelligence Community (147)

sachoudhury explains GitHub Copilot Custom Skills: repo- or user-scoped SKILL.md “runbooks” that Copilot can discover and execute in agent mode to automate multi-step developer workflows (commands, scripts, and report generation).

ManishChopra outlines six practical integration patterns for building agents and copilots that query Oracle Database@Azure with sub-millisecond proximity to Microsoft’s AI stack, covering options from Copilot Studio connectors to ORDS/PL/SQL, Azure Functions, and Logic Apps, plus the identity/governance controls typically needed for production.

jordanselig shows how to add runtime governance to a multi-agent ASP.NET Core travel planner on Azure App Service using the Microsoft Agent Governance Toolkit, including YAML policy allowlists, audit logging into Application Insights, and SRE controls like SLOs and circuit breakers.

mosiddi explains how Microsoft’s open-source Agent Governance Toolkit implements production-grade security and reliability controls for autonomous AI agents, covering its package architecture, policy enforcement (Agent OS), zero-trust identity (Agent Mesh), privilege rings (Agent Hypervisor), and SRE/observability integrations, including Azure deployment patterns.

jordanselig shows how to instrument Microsoft Agent Framework agents with OpenTelemetry GenAI semantic conventions and send that telemetry to Azure Application Insights, enabling the Agents (Preview) view for per-agent token usage, latency, errors, and end-to-end agent runs across an ASP.NET Core API and a WebJob.

jordanselig walks through building an MCP App (a tool plus a UI resource) with ASP.NET Core, rendering an interactive weather widget inside chat clients like VS Code Copilot, and deploying the MCP server to Azure App Service using azd and Bicep.

Shamir_AbdulAziz describes how Microsoft built Azure SRE Agent—an AI-powered ops agent—using “agentic workflows” across the SDLC, with human-in-the-loop governance, RBAC guardrails, and deep integration into telemetry and incident systems to reduce on-call toil and speed up incident mitigation.

Lee_Stott walks through what Azure Developer CLI (azd) is, why it’s useful for beginners, and how the AZD for Beginners learning path helps you move from local code to a repeatable Azure deployment workflow with templates, infrastructure as code, and lifecycle cleanup.

AnjaliSadhukhan argues that AI agents fail on enterprise questions mainly due to fragmented data and missing semantics, and outlines how Microsoft Fabric (OneLake, semantic models, Data Agents) and Azure AI Foundry can work together to provide governed, agent-ready access to business data.

Gaurav-Seth describes a hands-on, AI-guided workflow for migrating legacy IIS-hosted ASP.NET Framework apps to Managed Instance on Azure App Service, including how registry, storage, SMTP/MSMQ, and COM dependencies are handled via ARM templates and an install.ps1 startup script.

deepthihr walks through a real production incident running a private, enterprise AI platform on Azure, showing how DNS and private networking gaps (custom DNS, Private Endpoints, and Azure Container Apps internal ingress) caused intermittent failures—and the concrete fixes that stabilized the environment.

Pamela_Fox walks through implementing Model Context Protocol (MCP) server authentication with Microsoft Entra ID using the pre-registered (pre-authorized client) path, including Entra app registration setup, token validation in FastMCP, and an optional on-behalf-of flow to call Microsoft Graph securely.

macalde shares the March 2026 Innovation Challenge results, highlighting hackathon winners and example projects focused on building AI solutions on Azure (including RAG, multi-agent analytics, and governed AI outputs).

ShivaniThadiyan explains how Azure SQL Managed Instance is evolving from a SQL Server-compatible PaaS into an AI-enabled platform, covering built-in operational intelligence, vector search, in-database Python/R machine learning, and Copilot-assisted diagnostics with security and governance considerations.

ShivaniThadiyan outlines a shift-left approach to Azure infrastructure validation, using GitHub Copilot as an assistive layer to summarize Terraform plans, interpret drift signals, and help prioritize Azure Policy and Azure Resource Graph findings—without removing human approvals or governance.

Vaibhav Pandey shares a production-oriented “Bring Your Own Model” (BYOM) pattern for Azure AI applications, showing how to package, register, and deploy a custom model on Azure Machine Learning with secure identity, networking, and scalable managed endpoints.

Mayunk_Jain explains how Azure SRE Agent’s “active flow” charges are changing from time-based billing to token-based billing on April 15, 2026, including what stays the same (AAUs and always-on pricing) and where to find current per-model rates and usage controls.

dchelupati announces that Azure SRE Agent now lets you choose a model provider (Azure OpenAI or Anthropic Claude), aimed at improving complex incident investigations that correlate logs, deployments, and metrics into a single workflow.

lily-ma explains how to connect an MCP server hosted on Azure Functions to an Azure AI Foundry agent, covering why you’d do it, the main authentication options (keys, Entra ID/managed identity, OAuth passthrough), and the high-level steps to register the MCP endpoint as a tool and test tool-calling in the agent playground.

SundarBalajiA explains how to embed GitHub Copilot custom agents in a repo (via `.github/agents/`) to run Terraform-based Azure infrastructure security checks inside VS Code, including recommended agent metadata, tool permissions, and a structured finding format mapped to CIS, Azure Security Benchmark, and NIST controls.