This glossary is our attempt to ground the terms that keep coming up without clear, consistent explanations. It is not meant to be a comprehensive dictionary of every term in the field. Instead, we focus on the concepts that are often mixed up, reused in different ways, or assumed to be obvious when they are not.

Most of these terms come up whether you're building an agent, deploying one, or just using tools like Claude Code, Codex, or Hermes Agent. The last section covers concepts specific to training models, which is more relevant if you work on that side of things.

Many of these terms don't have universally accepted definitions yet, and different frameworks use the same word differently. The goal here is not to enforce one correct vocabulary, but to provide a practical mental model that makes discussions easier to follow.

The model is the LLM: it takes text in and produces text out (e.g., Claude, Qwen, GPT, Kimi, DeepSeek…). On its own, it has no memory between calls, and no loop. The model can express the intent to call a tool, but it needs a harness to actually execute it. It answers one prompt and stops. Wrap it in scaffolding and a harness and it becomes an agent.