Browse Artificial Intelligence Blogs (51)

Rob Bos introduces the GitHub Copilot App technical preview and shares a practical first look at using it for repository maintenance, including parallel agent sessions, session modes (Interactive/Plan/Autopilot), and the Agent Merge workflow for handling CI failures, merge conflicts, and security-related alerts.

John Edward explains how GitHub Copilot changes team workflows around pull requests, code review expectations, and knowledge sharing. The article focuses on the trade-offs of faster AI-assisted coding, why review discipline matters more, and how teams can add guardrails like testing and security scanning without losing collaboration.

Jesse Houwing breaks down why GitHub Copilot is moving from Premium Request Units to token-based, usage-based billing, and what that means for model selection, cost predictability, and newer features like Agent Mode, Cloud Coding Agent, and Copilot Code Review—especially for organizations managing budgets and policies.

John Edward outlines an architecture for a “Daily Stand-Up Agent”: a custom AI copilot that pulls sprint activity from Jira and Azure DevOps, detects blockers, and generates consistent stand-up summaries. The post focuses on connectors, grounding ticket data, conversational reporting, and practical considerations like security and data quality.

Rob Bos shares a real-world GitHub Copilot CLI mishap where an unintended Copilot CLI extension caused repeated prompts to close GitHub deployment-status notifications, and explains how he tracked down the source and removed it.

DevClass reports on the Zed editor reaching version 1.0, covering its Rust-based architecture, GPU-accelerated UI, built-in language server support, and the editor’s growing set of AI features (including agents) alongside an option to disable AI entirely.

John Edward explains how Architecture Decision Records (ADRs) capture the “why” behind technical choices, and how AI tools can generate consistent ADR drafts quickly so teams can focus on review, accuracy, and long-term knowledge sharing.

John Edward breaks down the core building blocks of copilot agent systems, explaining how interface, orchestration, LLMs, tools, memory, and safety layers fit together. The article also covers common design patterns like RAG and tool-using agents, plus practical challenges around context, reliability, latency, and security.

Rob Bos shares an overview of his open source projects spanning GitHub and CI/CD tooling, Azure-backed services, security reporting, and local-first AI utilities, with links to each repo and a clear description of what each tool does.

Hidde de Smet shows how to combine five GitHub Copilot customization file types in a single .NET Aspire repo, so the right instructions, skills, prompts, and agent roles load at the right time without bloating every chat request.

John Edward discusses how GitHub Copilot changes programming education, where it can speed up learning, and where it can undermine fundamentals if students rely on it too heavily. The post outlines practical habits for students and classroom approaches for educators to use Copilot without losing academic rigor.

Rob Bos breaks down five GitHub Copilot and agent extensibility surfaces that create supply-chain and governance gaps in large enterprises, and explains what controls exist today (and where they don’t) across Copilot CLI plugins, APM, gh skill, MCP servers, and VS Code extension registries.

Hidde de Smet's Blog breaks down the difference between AGENTS.md (repo-wide, always-on instructions for coding agents) and .agent.md (custom agent profiles for GitHub Copilot), including where to place each file, what fields matter, and how to use roles, tool restrictions, and handoffs safely.

John Edward explains why event-driven architecture is a strong fit for agentic AI systems, and breaks down the core patterns (pub/sub, event sourcing, sagas) plus practical concerns like ordering, observability, and infrastructure overhead.

Hidde de Smet's Blog explains how GitHub Copilot “skills” work via SKILL.md folders, why the YAML description is the key to discovery, and how this approach keeps context lightweight compared to a giant copilot-instructions.md. It includes a practical Azure Monitor/Application Insights KQL skill you can copy into a repo.

DevClass.com reports on Visual Studio 18.5 (Visual Studio 2026), covering new Copilot-driven “agentic” debugging, changes to how IntelliSense/Copilot suggestions are prioritized, and ongoing developer complaints about theme contrast and forced auto-updates.

Hidde de Smet compares three AI coding setups—single-agent, agent-with-tools, and multi-agent—using a realistic .NET Aspire + ASP.NET Core rate-limiting task to show trade-offs in fit, cost, latency, and common failure modes.

John Edward explains when to use single-agent vs multi-agent AI architectures in a Microsoft context, mapping common designs to Semantic Kernel, AutoGen, and Azure services like Azure OpenAI, Azure AI Search, Functions, Service Bus, and AKS.

DevClass.com reports on GitHub’s private preview of Stacked PRs, a workflow for breaking large changes into smaller, independently reviewable pull requests that can still depend on each other, with an optional gh stack CLI that’s also intended to work well with AI agents.

Jesse Houwing summarizes GitHub’s update that GitHub Copilot can now keep inference processing and associated data within US or EU data residency regions, and shows the enterprise/org policy you must enable to restrict Copilot to data-resident models.

Rob Bos walks through running GitHub Copilot CLI against local OpenAI-compatible inference servers (Ollama, LM Studio, Foundry Local, vLLM/TGI), focusing on the practical constraints (32k context, tool calling, VRAM/KV-cache) and sharing concrete Windows/PowerShell setup and throughput numbers.

Emanuele Bartolesi shows how to point GitHub Copilot CLI at an Azure AI Foundry (Azure OpenAI) deployment using a BYOK-style setup, including how to deploy a model, build the correct endpoint URL, set the required environment variables, and validate the connection.

Emanuele Bartolesi explains how to run GitHub Copilot CLI against a local LLM via LM Studio’s OpenAI-compatible API, including the exact PowerShell environment variables needed to avoid cloud fallback and when this offline setup is (and isn’t) worth using.

Hidde de Smet explains how Spec-Kit’s extension system works, highlights useful community extensions, and walks through the Ralph Loop extension, which runs a GitHub Copilot agent in iterations to implement tasks from `tasks.md`, commit changes, and track context in `progress.md`.

Harald Binkle explains the latest Visuals MCP update, adding a chart tool that lets AI agents render single charts and full dashboards directly inside GitHub Copilot Chat in VS Code, with Storybook examples and export options for turning analysis into shareable visuals.

Thomas Maurer introduces Azure Local Disconnected Operations and explains how to run Azure-style infrastructure—and selected AI workloads—inside fully disconnected or air-gapped environments for sovereignty and compliance needs.

John Edward outlines a practical security checklist for running Microsoft AI agents in production, covering Entra ID identity controls, least-privilege access, data boundaries and DLP, audit logging with Azure Monitor/SIEM, and concrete defenses against prompt injection and unsafe agent behavior.

Randy Pagels explains a simple GitHub Copilot workflow: before asking for an implementation, prompt Copilot to ask clarifying questions so you uncover assumptions, edge cases, and missing requirements early—leading to better prompts and better code changes.

Jesse Houwing clarifies GitHub Copilot’s April 24 interaction-data policy change, explaining which subscription tiers may have interactions used for training, what is and isn’t included (like private repos), and practical ways enterprises can enforce license tiers and lock down developer environments.

Emanuele Bartolesi explains how to make GitHub Copilot less “agreeable” and more useful by adding a repo-level voice instructions file that pushes Copilot to be direct, critical, and focused on correctness and maintainability.

Zure summarizes recent Microsoft Fabric and Purview capabilities for metadata management and governance, covering OneLake catalog search, workspace tagging, bulk definition APIs, and how AI agents/copilots intersect with lineage, compliance, and risk controls.

John Edward shares practical ways to control Azure-based copilot and AI agent spend, focusing on token discipline, caching, model selection, and ongoing governance so LLM solutions scale without surprise bills.

Jesse Houwing explains why he rebuilt the Azure DevOps Marketplace publishing tasks from v5 to v6, focusing on faster builds, stronger testing, GitHub Actions support, and more secure authentication (OIDC/workload identity) while using GitHub Copilot’s Coding Agent to accelerate the rewrite.

John Edward compares Microsoft Copilot Studio and Azure AI Agents (via Azure AI Foundry/Studio) to help architects choose between a low-code agent builder and a developer-driven platform based on flexibility, cost, scalability, and control.

Thomas Maurer summarizes Commvault’s expanded integration with Microsoft Security, bringing Commvault recovery-layer signals into Microsoft Sentinel and adding an Investigation Agent for Microsoft Security Copilot to speed up investigation and clean recovery during ransomware incidents.

Bruno Van Thournout's Blog argues that large language models are shifting software work toward “natural language as code,” and explains the practical engineering trade-offs: nondeterministic outputs, unstable execution layers as models change, and the need to treat prompts as versioned, testable artifacts.

Heidi Hämäläinen explains why Microsoft Purview Data Governance can feel heavy at first, and why governed metadata (glossary, catalog, data products, and security foundations) matters for scalable analytics, ML, and GenAI work—especially when you need discoverability, compliance, and trust in production.

Randy Pagels shares practical tips for developers to maximize GitHub Copilot's effectiveness by providing better context and intent, rather than relying on longer prompts.

DevClass.com highlights Microsoft's switch to weekly Visual Studio Code releases and the rollout of Autopilot in Copilot Chat, offering developers new AI-driven coding experiences while raising fresh security concerns.

John Edward explores the foundations of Microsoft Copilot agent design, outlining how goals, memory, tools, and autonomy create robust, autonomous AI systems for enterprise automation.