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Introducing the Agent Experience (AX) Practitioner Playbook
AI coding agents now pick the SDK, the version, and the pattern for your developers. The AX Practitioner Playbook is our method for finding out whether they get your technology right, and for fixing the sources they rely on when they don't.
From Spec-First to Enterprise-Ready: Extending GitHub Spec Kit
How presets, extensions, bundles, workflows, and governed catalogs make Spec-Driven Development adaptable at scale This article continues Spec-Driven Development: A Spec-First Approach to AI-Native Engineering, which introduced the principles and lifecycle of SDD. Here, we focus on the next challenge: adapting that workflow to the standards, domains, tools, and governance needs of a large engineering organization. When a good workflow meets organizational reality Spec-Driven Development (SDD) gives teams a durable source of truth: structured specs that carry intent through requirements, design, implementatio...
Build Azure canvases with the Canvas authoring plugin
Learn how the Canvas authoring plugin helps you build Azure canvases with Microsoft Canvas Toolkit, from a read-only starter to a working app you can inspect, test, and adapt.
What is Agent Experience (AX)?
Your agent says it's done, and the code compiles, but did it pick your technology? Did it use it correctly? Learn what Agent Experience (AX) is, how to measure it, and why the obvious fix isn't always the right one.
Build with Azure Canvases: a shared workspace for agents
See how Azure Canvases bring plans, code, previews, and deployment feedback together in a collaborative workspace for agent-assisted development.
Enabling Consistent AI-Assisted Engineering with GitHub Copilot Plugins
When an engineer or team creates useful agentic artifacts for their daily work (e.g., instructions, skills, and MCP configurations), others naturally want to adopt them when they see how those artifacts could help with their own work. Sharing the files is straightforward, but keeping those consumers supplied with fixes, updated guidance, and compatible tool configurations requires a way to maintain and distribute those artifacts. That challenge shaped our work with a large enterprise customer as we designed and built a migration and modernization toolkit together. Their internal and third-party engineering tea...
Get started with the GitHub Copilot app: a free, hands-on course
Learn to direct AI coding agents, review their work, and build reliable development workflows with this free GitHub Copilot app course.
Knowledge cutoff is a poor proxy for model capability
A model can fail on features released before its knowledge cutoff, then succeed on ones released after it. We tested hundreds of product changes and found that the date tells you far less than the work does.
Your AI coding agent evaluation is only as good as its sandbox
Your AI coding agent passed the eval. But did the model know the answer, or did it find it somewhere on your machine? A correct answer can still invalidate your measurement.