There are four layers to the agentic AI stack right now. By next quarter, this model will probably be wrong. But it's useful today, and that's enough.
I'm using the Claude ecosystem as my reference point. It's leading the open source charge, everything is transparent and analyzable, and Opus 4.5/6 are excellent agentic models.
Layer 1: The Agent
There are two kinds of AI agents right now. I call them "basic" and "autonomous."
The basic agent is vanilla Claude Code. It lives on your machine, it has no opinions, and it doesn't wake itself up to do scheduled work. But it's customizable to infinity by just talking to it. Tell it to improve itself and it will. If you can't do that with an agent, it's not "agent-complete."
The most popular autonomous agent is OpenClaw (formerly Molty). Think of it as Claude Code with a personality. It has a SOUL.MD file that defines its beliefs and character. It has a heartbeat through cron jobs. It has built-in integrations for almost any product, a marketplace for more (Clawdhub), and a social network for agents (Moltbook).
Layer 2: Skills
There are thousands of skills, plugins, and MCP integrations available, with more shipping every day. Skills are the most interesting layer right now because they're universal. They work for both vanilla Claude Code and OpenClaw.
Skills come in two flavors.
Professional skills make your agent better at something specific. What does "better" mean here? It means opinionated. Vanilla Claude is already good at most knowledge work. The gap between "good" and "excellent" is closed by giving it precise directions.
The best professional skills are real expertise distilled into prompts, packaged as SKILL.MD files that agents load into context. A senior developer's hard-won opinions about error handling and architecture. A designer's principles about layout and typography. These files turn a generalist into a specialist.
Meta skills are where it gets interesting. Say you've loaded all the best dev skills. Your agent is, on paper, an excellent senior developer. But it still struggles to work autonomously on your project. Why?
Because it has book knowledge without experience. Meta skills are a form of reinforcement learning. They close that gap.
A few examples.
Ralph Wiggum is a meta skill that traps the agent in a loop until it hits a condition you set. It will keep grinding at your problem until it cracks it or runs out of tokens.
Self-improvement: the agent tracks its own repeating patterns and turns them into new skills automatically.
Reinforcement learning: the agent logs every mistake and learns not to repeat it. The more you use it, the better it gets.
Stylistic modification: your content agent writes like a robot. You give it a skill that catches AI patterns and replaces them with your own voice. Now it writes like you.
What to Do With All This
If you haven't built yourself an agent yet, here's the path.
Start with vanilla Claude Code, not OpenClaw. OpenClaw is powerful but genuinely dangerous. If you don't know what you're doing, you will install malware within a week. Vanilla is safe to learn on.
Model your first agent on your own job. Pick skills and workflows that you can personally verify. If you're a developer, make a dev agent. If you're a designer, make a design agent. Don't build what you can't evaluate.
Go find professional skills. Search X, GitHub, or just ask Claude Code directly. Focus on one niche. If your designer agent needs Figma, add Figma. But read every skill file before you install it. There are malware skills floating around. Your agent has access to your machine. A bad skill can rob you.
Then add meta skills. At minimum, set up reinforcement learning. You can do it in one sentence: "Keep a LESSONS_LEARNED.MD file and update it every time you make a mistake or learn something important." That's it. Your agent now gets better over time.
Layer 3: Orchestration
We have good individual agents. How do we make them work as a team?
That's what the world is figuring out right now. Some people are building orchestration systems: agent factories that produce code or content at scale. Others are exploring the social angle, where agents talk to each other on specialized networks. That rabbit hole deserves its own post.
I'm working on this layer myself. I won't pretend to have it figured out yet.
What Comes Next
Every new model and every Claude Code update comes with more of these patterns already built in. The agents of tomorrow won't need us to pick skills for them. They'll become whatever role is needed on the fly.
Professional skills will go obsolete first. Meta skills after that. The whole "skill" ecosystem we're building right now probably has a month or two of relevance.
After skills collapse, the action moves to Layer 3. Orchestration is still young.
Layer 4 will be AI-led organizations.
I don't know what Layer 5 is yet.
I have a hunch that somewhere around Layer 7, we're looking at something like a single, networked intelligence. That's science fiction today. Ask me again in a year or two.
