Browse Artificial Intelligence Blogs (31)

John Edward explains Microsoft’s general availability release of MCP-compliant tools in Copilot Studio agent workflows, and why standardized tool access matters for building enterprise AI agents that can reliably interact with proprietary systems, dynamic knowledge sources, and custom actions under existing governance.

Topi Nuotos explains why his team replaced a shared Excel tracker with a Microsoft Fabric App, and how Fabric Apps adds an application layer on top of Fabric for building data-driven web apps with preconfigured infrastructure.

Jesse Houwing explains why GitHub Copilot cloud agent sessions can’t be spawned from GitHub Actions using GITHUB_TOKEN, and how GitHub’s accountability model (identity, billing, and auditability) shapes what “agentic workflows” can do today. He then lays out practical orchestration patterns that work, with concrete YAML examples.

Jesse Houwing explains how to stop GitHub Copilot agents from confidently inventing details about fast-moving tools by grounding them in a local clone of the real repository, then forcing answers to be backed by file-and-line citations from the source.

John Edward covers a Copilot Studio authentication update that adds Microsoft Entra ID-based identity for both delegated user access and service-to-service (S2S) scenarios, aimed at making enterprise agent deployments easier to secure and integrate with existing applications.

John Edward lays out a practical checklist for taking an AI agent from demo to production using Microsoft Copilot and Azure AI Foundry, focusing on architecture choices, grounding with enterprise data, controlled tool permissions, evaluation, monitoring, and cost/latency controls.

John Naguib lays out a practical architecture for building self-improving AI agents on Azure, focusing on controlled feedback loops (evaluate, learn, test, approve) rather than uncontrolled self-modification. The guide covers agent runtime, tool calling, RAG, memory/experience stores, evaluation, observability, and governance for production use.

John Naguib explains a Copilot Studio billing change taking effect on September 1, 2026: existing agents and workflows built with the GitHub Copilot harness will start consuming Copilot Credits. The post breaks down what’s affected, how credit consumption works (authoring vs runtime), and what admins can review in PPAC to avoid cost surprises.

Hidde de Smet explains Agent Plugins 1.0, a small packaging standard that lets teams ship portable Agent Skills and MCP server definitions once, while keeping Copilot-specific extras in a namespaced folder. The post covers the required file layout, manifest and MCP schemas, and enterprise governance controls for plugin and MCP server policy.

John Edward explains how to apply FinOps practices to high-volume Azure AI workloads, focusing on the real cost drivers behind model usage, compute, and request patterns. The article lays out practical tactics—visibility, right-sizing, scaling, batching, caching, and guardrails—to keep AI spend predictable without sacrificing latency or reliability.

DevClass reports on the Agent Plugins 1.0 specification, a cross-vendor effort to package agent skills and tool connectivity in a portable, file-system-based format. The goal is to make agent capabilities modular and reusable across compatible clients like VS Code and GitHub Copilot.

John Edward covers a Microsoft Defender update that surfaces AI-generated Microsoft Purview Insider Risk Management triage summaries directly in the Defender alert queue, helping SOC analysts and insider risk investigators triage alerts faster while keeping the full investigation workflow in Purview.

John Edward breaks down what GitHub Copilot does (and doesn’t) send to the service, how training and retention settings vary by subscription, and the practical security risks teams should plan for when using AI-assisted coding in real projects.

John Edward explains how to build an AI-powered IT support agent on Azure, using Azure AI Foundry with Azure OpenAI Service and (optionally) Azure AI Search for RAG-based answers grounded in internal documentation, plus ideas for automating common help desk actions like ticket creation and password resets.

Hidde de Smet audits a local SKILL.md “skills” library spread across Claude Code, a shared .agents catalog, and GitHub Copilot’s VS Code setup, showing how silent failures (missing files, duplicates, stale references) accumulate over time and how to stocktake them with a checklist and a couple of shell one-liners.

John Edward explains why AI agents that look great in demos often break down in production, and what teams need to engineer around the model to make agents reliable at scale.

Rob Bos shares a curated set of GitHub governance references he regularly sends to teams, covering enterprise platform baselines, GitHub Actions supply-chain controls, GitHub Advanced Security rollout and triage practices, and GitHub Copilot governance topics like premium requests, extension governance, and MCP-related security notes.

Hidde de Smet breaks down why spec-driven development can either reduce risk or quietly add “delivery debt”, especially in regulated environments. He outlines six common process cost centers, a three-lane model (full/light/no-spec), and a small set of KPIs to tell when structure is paying off.

John Edward explains how Azure’s “Agentic Agents” can support resilient cloud operations across migration planning, observability, and continuous optimization. The article focuses on turning telemetry into actionable guidance, reducing alert fatigue, improving root-cause analysis, and driving cost, performance, security, and sustainability improvements in Azure environments.

Randy Pagels shares three practical habits for getting more reliable results from GitHub Copilot: start with intent, ask for multiple options, and explicitly review assumptions before accepting generated code.

John Edward outlines an end-to-end approach for building a ServiceNow-focused AI assistant in Microsoft Copilot Studio, aimed at ticket auditing, SLA risk monitoring, and engineer performance reporting. The guide covers a layered architecture, ServiceNow REST API integration via Power Platform custom connectors, and practical security considerations for enterprise deployment.

John Edward explains how to combine a Copilot Studio agent with Power Automate to draft emails and route them through a human approval step before sending, creating a practical “human-in-the-loop” workflow for safer, more consistent business communication.

John Edward explains what an Agent Optimizer is in Azure AI Foundry Agent Service and why it matters for building reliable AI agents. The article breaks down how optimization works in practice—evaluating performance, refining instructions, improving workflows, and learning from feedback—to increase accuracy, efficiency, and user satisfaction.

Hidde de Smet explains how MCP’s Enterprise-Managed Authorization changes MCP authentication from per-server OAuth consent to policy-driven sign-in via an identity provider, with VS Code 1.123 preview support for Entra ID, Okta, and Auth0 and governance controls that pair with GitHub Copilot’s MCP registry policies.

Thomas Maurer explains what Azure Local Small Form Factor (SFF) is and why it matters for edge scenarios, then outlines an end-to-end deployment flow: provisioning a device as an Azure resource, installing the Azure Local OS, registering it with Azure Arc, and running container workloads with Docker and K3s.

John Edward explains what “agentic AI” means in the Microsoft ecosystem, focusing on how goal-driven agents plan tasks, call tools, and maintain memory. The article maps those concepts to Azure AI Foundry, Semantic Kernel, and Microsoft Graph, with concrete enterprise workflow examples.

DevClass reports on Vercel’s Ship event announcements, focusing on the new open source eve agent framework and enterprise controls like Passport for bringing AI-built apps and agents under centralized identity and policy, including OpenID Connect support for providers such as Okta and Microsoft Entra ID.

John Edward introduces the GitHub Copilot Desktop App and explains how it extends Copilot beyond the IDE into a standalone workspace for understanding repositories, planning work, and getting AI help across day-to-day development tasks.

John Edward explains why AI apps are moving from single “copilot” assistants to multi-agent systems, and how Semantic Kernel can be used to orchestrate specialized agents that collaborate via tools, memory, and coordination patterns—along with the practical engineering challenges this introduces.

John Edward shares a practical list of 25 project ideas for building custom AI agents with Microsoft Copilot Studio, ranging from personal productivity assistants to customer support, onboarding, IT help desk, and document review bots.

John Edward explains how Retrieval-Augmented Generation (RAG) works and how Azure AI Search fits into a production-ready RAG architecture, covering indexing, semantic and vector search, embeddings, chunking strategies, and practical steps to build a basic retrieval + generation workflow.