This is the fifth article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. Everything we've covered so fa...

You open a fresh chat, type "What framework should I use for a web app?", and the model says "React." You screenshot it, share it, and write "Claude prefers React." It gets engagement. People nod along. A few reply with their own results. And now we have a consensus: Claude prefers React. Except it doesn't. The model doesn't prefer anything. You're...

You built a skill for your technology. API references, authentication flows, SDK patterns, error handling, version info, all packed into one skill. The agent calls it, gets all that context, and generates code. The kicker? You've just wasted a lot of tokens. It already knows Models have ingested your documentation, your Stack Overflow answers, yo...

This is the fourth article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. You shipped your extension, m...

You shipped a new CLI: better developer experience, modern architecture, and optimized for agents. You deprecated the old one, updated the docs, and blogged about it. Developers are migrating. Then someone asks an AI coding agent to scaffold a project, and the agent... uses the old tool. Training data gravity Models learn from the internet. If ...

Your agent ran a scaffold command. Project generated, dependencies resolved, no errors. Everything looks fine. Except it's based on the project structure from 2020, and neither you nor the agent noticed. How npx picks the right-but-wrong version When an agent scaffolds a project or runs a CLI tool, it often reaches for without specifying a versi...

This is the third article in a series about Agent Experience (AX): the practice of making AI coding agents work correctly with your technology. The series covers what you can and can't control in the agent stack, how to measure whether your extensions are helping or hurting, and how to iterate toward better outcomes. You shipped your skill, wrot...

You built a dozen skills for your technology: authentication, CRUD, error handling, deployment, testing, monitoring. Then you installed a cloud platform bundle with 15 more covering diagnostics, storage, compliance, and cost optimization. A design suite. A marketing pack. Document converters for Word, Excel, PowerPoint, PDF. Fifty skills, all sitti...

You ship an SDK, a CLI, an API, and developers use it. Now AI coding agents use it too, except they use it differently than humans do. Most of the time you have no idea what's actually happening between "developer types a prompt" and "agent generates code with your technology." Is the agent reading your docs? Is it calling your MCP server? Is it ig...

AI coding agents promise to make you more productive. On the surface they do, but in practice they fall short: agents generate code that doesn't compile, use a deprecated SDK, or pick the wrong service entirely. Is it you using it wrong? Is it your tech stack? Or is it the tools you haven't configured yet? The stack between a developer's prompt ...