A client recently asked me a really good question: “Is there any AI method that canA client recently asked me a really good question: “Is there any AI method that can
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A client recently asked me a really good question:
“Is there any AI method that can help us manage or update our Webflow website faster?”
Most clients don’t want to depend on a developer every time they need to update a CMS item, improve a meta title, publish a blog post or make small content changes.
They don’t want to rebuild the website.
They just want a faster way to manage it.
That is where Webflow and Claude MCP become interesting.
With the right setup, AI can help non-technical teams work with Webflow content faster, especially when it comes to CMS updates, content editing and SEO improvements.
Of course, this does not mean giving AI full control without checking anything.
But it can make the content workflow much smoother.
I shared more about this topic here:
https://atado.studio/blog/webflow-claude-mcp-content-updates
Both platforms excel at visual development, but their modern use cases are distinct:
• 𝗙𝗿𝗮𝗺𝗲𝗿: Best for rapid AI layout iteration, SaaS conversion pages, and Figma-native auto-layout fidelity.
• 𝗪𝗲𝗯𝗳𝗹𝗼𝘄: Unmatched for complex relational database logic, multi-locale localization, and enterprise governance. Here is how to choose between them in 2026.
I’ve been working on a small skill called Manual Orchestrator to make longer projects with AI agents easier to manage.
The problem started with a familiar situation: one chat would accumulate plans, implementation details, corrections, and test results. By the time I was ready for the next phase, I wanted a fresh conversation without losing the decisions we’d already made.
Manual Orchestrator gives that process some structure. One chat acts as the orchestrator: it breaks the work into stages and writes a prompt for the next one. I run that prompt in a new session, then bring the result back. The orchestrator reviews it against the stage’s criteria before preparing the next prompt. If something needs fixing, I go back to the same worker session.
The skill also keeps a record in the project with the current stage, decisions, evidence, and next steps. That makes it easier to pause, resume, or hand the work to another session.
The handoff is manual. I choose when to open a session and carry the result back. The orchestrator and worker sessions can also use different agent CLIs, with the prompt adapted to the capabilities available.
It’s open source and still a work in progress. I’m using it to learn what helps in practice and where the workflow needs to improve. Installation has been checked for Codex, Claude Code, and Gemini CLI, but the full workflow hasn’t been tested across every CLI or version.
If you try it, I’d be interested in hearing what works and where it gets in your way.
I like that the handoff is manual. A written record with decisions and next steps is what I miss most when a long AI chat gets messy. How do you decide when a stage is finished: with tests, or with your own review?