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LLBBL Blog

A Field Glossary for Agentic Knowledge Work

Everywhere you look right now, somebody is saying agentic this and agent that. Harness, scaffold, skill, subagent, agentic OS. The vocabulary is piling up faster than anyone can keep track of, and a lot of it gets used loosely, sometimes by people who don’t actually know what the words mean.

So I figured I’d write down my own working glossary. This isn’t a textbook, and I’m not pretending these are official definitions. It’s how I think about the terms when I’m doing the work. If you’ve been nodding along in conversations without being totally sure what a harness is, this one’s for you.

Agent

There are a lot of flavors of agent, and I’m not going to catalog all of them. Generally speaking, an agent is some code wrapped around an LLM that runs in a loop, has access to tools, and can act using those tools.

That’s the key difference from a plain prompt-and-response call. A direct call to the model gives you one answer and stops. An agent has some degree of autonomy. It can decide to use a tool, look at the result, and keep going. The loop and the tools are what make it an agent instead of a chatbot.

Harness

A harness is the program that sits on top of the model. It manages conversation state, runs the reasoning loop, gives the model access to tools, and enforces the guardrails, things like permissions, controls, and budget.

Here’s an easy way to understand it. The model is the intelligence. The harness is the control on the intelligence. The harness sits between you and the LLM.

A harness can show up in a lot of places. It might be a CLI. It might be a GUI or an app on your phone. It might be a chat thread. You could wire up something like OpenClaw to talk to you in WhatsApp or Telegram, and that chat becomes your harness, while OpenClaw is also a harness underneath. So yes, harnesses can call other harnesses. It’s turtles a little way down.

Scaffold

You’ll hear people say scaffold or scaffolding. This is usually just another word for harness. The prompt, the tools, and the control structure wrapped around the model. Same idea, different label.

Framework or SDK

These are the libraries you build harnesses with. LangChain, the various agent SDKs, or a ready-to-run harness like Claude Code or Hermes.

Worth flagging that framework and SDK mean something specific in regular programming. In the agentic context they’re a little looser. They’re what you build agents and harnesses out of. And it doesn’t have to be off-the-shelf. You can absolutely build your own framework for building your own harnesses if that’s where your head is at.

Context Engineering

This is the big one. The term comes from Karpathy, and while it mattered even more a year ago than it does today, it still applies.

Context engineering is deliberately managing what’s in the context window of the current session. It’s the work of deciding what gets loaded into context and, just as importantly, what gets left out. It’s the successor to what we used to call prompt engineering. The framing shifted because the prompt is only one piece of what the model sees, and the rest of it matters a lot.

MCP

Model Context Protocol. I won’t go deep here, it deserves its own post. MCP is an open protocol for exposing tools and data to an LLM harness. It’s the standard way for your harness to reach out and use third-party software or pull in outside data.

Skills

Skills are a major, important thing, and I’m not going to do them justice in a glossary entry. But here’s the definition.

A skill is a reusable, often self-created capability that bundles up the instructions an agent needs to accomplish a specific task. You can find skills all over the internet now. Everybody’s got their own. You can generate your own pretty easily with the CLI, and harnesses like Claude Code or Hermes can even author their own skills. The word does the work here. Skills are capabilities. It’s how you extend what your agents and harnesses can actually do.

Subagent

A subagent is an isolated child agent spawned from inside a working session.

Say you’re in your harness of choice with a main session running. That orchestration session can fork off a new agent with its own context window, hand it a specific task, and say go do this. The subagent runs on its own, often in parallel with others, working in the background. The main session knows when it finishes and can check the work.

A lot of the time you’ll have a second subagent review the first one’s output. That review loop is the whole idea behind the agentic maturity model, which is a way of thinking about how to structure this kind of work. It’s on GitHub if you want to dig in.

Agentic OS

This is an orchestration layer that combines agents, memory, and tools. It’s not really an operating system, but the name has stuck. You take all these concepts, the skills, the agents, the memory, the tools, and combine them into one organized whole. People are calling that amalgamation an agentic OS.

Second Brain / PKM

A personal knowledge vault. I posted about this just this week. It’s a personal knowledge base that the model can read, search, and extend. Your notes, your references, your accumulated thinking, made available to the agent.

Vibe Coding vs. Agentic Engineering

This is the distinction I care about a lot.

Vibe coding is not really knowing what you’re doing or how it’s being done. Anyone can vibe code. You describe what you want and you accept what comes back.

Agentic engineering is knowing what you’re doing and caring about how it gets done. Not everyone can do that part.

The way people put it is that vibe coding raises the floor and agentic engineering raises the ceiling. Vibe coding lets anyone build something. Agentic engineering lets a professional move a lot faster than they used to. Both are real. They’re not the same thing.

That’s the glossary, at least the version that lives in my head. None of these terms are settled, and half of them will probably mean something slightly different in six months. But hopefully this has helped!

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Sources

AI Agents Agentic LLM Glossary