Storybook's official MCP server (@storybook/addon-mcp) turns your component library into a live knowledge source for AI agents. Agents can query component documentation, generate stories, run interaction and accessibility tests, and self-correct — all without you having to intervene. This guide covers the architecture, the three toolsets, installation step-by-step, and two alternative MCP approaches including the community-built storybook-mcp package.
Work IQ is Microsoft's new intelligence layer for Microsoft 365, announced at Build 2026 and going generally available June 16, 2026. It gives agents — and the developers building them — a way to work with business context rather than raw data: 2x faster than traditional APIs, 80% fewer tokens, 10 MCP tools instead of hundreds, and full M365 tenant security. This post covers the architecture, the five core advantages, how to get started with the MCP server and CLI today, and what it means for Power Platform developers building on Dataverse and M365.
Claude Fable 5 is Anthropic's first Mythos-class model and it's now generally available in GitHub Copilot. This breakdown covers the technical specs, the built-in Opus 4.8 fallback mechanism, the 30-day data retention requirement that breaks standard ZDR assumptions, and a practical enablement framework for Power Platform and enterprise teams operating under data governance constraints.
GitHub Copilot code review is entering limited public preview inside Azure Repos pull requests. This guide covers three-tier setup, billing on your Azure subscription, preview limits, and how to integrate AI-assisted code review into Power Platform ALM workflows for Dataverse plugins and PCF controls.
GitHub Copilot's Plan agent, introduced in Visual Studio in May 2026, adds a deliberate planning phase before any code generation happens. Instead of watching AI modify files in unpredictable directions, you review and approve a detailed Markdown plan grounded in your actual codebase — before a single file changes. This guide covers how the Plan agent works step by step, how to write prompts that produce genuinely useful plans, and how to apply it to a real Dataverse plugin refactoring scenario with working before-and-after code examples.
What if you could build canvas apps in 8 minutes instead of 45? What if version control, real IDEs, and CI/CD finally worked with Power Apps? I'm launching a 5-part series that shows you exactly how to do it—with AI-powered development tools that are changing everything.
Microsoft's Dataverse Skills represents a paradigm shift in Power Platform development—from manual tool orchestration to intent-driven AI coding. This open-source plugin for GitHub Copilot and Claude Code eliminates the developer tax of context-switching between PAC CLI, maker portal, and API documentation. Instead of writing scripts, developers describe their intent in natural language, and AI agents build complete Dataverse solutions automatically. Learn how this changes enterprise development workflows, why it matters for solution architects, and what skills-based development means for the future of low-code platforms.
Microsoft's Dataverse Skills transforms Power Platform development from manual tool orchestration to AI-driven intent. This comprehensive guide covers the complete journey: understanding the paradigm shift from scripts to natural language, installing and configuring for GitHub Copilot and Claude Code, mastering custom skill development with YAML frontmatter, and implementing enterprise governance with MCP billing controls and ALM integration. Whether you're a developer learning intent-driven workflows or an architect evaluating production readiness, this series provides the technical depth and real-world insights you need
GitHub Copilot is only as smart as the context it can see. This guide explains exactly how workspace context and codebase indexing work in VS Code — remote vs local vs basic indexes, when to use #codebase vs @workspace, how to write copilot-instructions.md for your team, and six prompt patterns that consistently produce better answers. Practical, code-first, no fluff.
Your AI agent just modified the wrong file. Again. Before you blame the model — when did you last check what instructions you actually gave it?
Most developers hand a powerful coding agent a vague persona and wonder why results are inconsistent. GitHub's analysis of 2,500+ repositories reveals the uncomfortable truth: the gap between agents that work and agents that guess comes down to one file you're probably not writing correctly.
In this guide: the six pillars of a high-performing agents.md, a production-ready Dataverse plugin agent template, and the maintenance habit that keeps your AI sharp as your codebase grows. Your agent is only as good as the manual you write for it.