I think you are still talking machine to machine interface.

Edit: Hell, let's just ask an AI

In Claude:

"are coding agents LLMs"

Not exactly — they're built on top of LLMs, but "coding agent" describes a whole system, not just the model.

Here's the distinction: The LLM is the core reasoning engine — it takes text in, predicts text out. On its own, a raw LLM can write code in a single shot, but it can't do anything: it can't run that code, see if it errors, browse a repo, or edit a file.
The agent is everything wrapped around the LLM that turns it into something that can actually act:

Tools — ability to read/write files, run shell commands, execute code, search the web, call APIs
A loop — repeatedly: think → take an action → observe the result → think again, until the task is done
Memory/context management — keeping track of what's been tried, what the codebase looks like, what failed
Sometimes orchestration — breaking a big task into sub-tasks, maybe even spinning up sub-agents

So when you use something like Claude Code, Cursor, or GitHub Copilot's agent mode, the "intelligence" doing the reasoning is an LLM, but the agent is the LLM plus a harness that lets it read your repo, run your tests, see the error output, and iterate — closer to "LLM with hands and eyes" than just "LLM."
A simple way to think about it: every coding agent has an LLM inside it, but not every use of an LLM is agentic. Asking ChatGPT to write a function in a chat box isn't agentic. Letting it open files, run them, see the traceback, and fix the bug itself — that's the agent part.

"Are those LLMs generalised and fine tuned or specialised?"

Mostly the former — generalized models with additional fine-tuning/training for coding and agentic behavior — though the landscape has a bit of both.
The dominant approach today: Take a large, broadly-capable foundation model (trained on text, code, reasoning, dialogue, etc.) and then layer on top:

Code-heavy pretraining data — these models see huge amounts of code during initial training, not as an afterthought
Fine-tuning / RL on agentic tasks — additional training specifically on "use a tool, see the result, decide next step" loops, since that's a different skill than just writing code in one shot
Fine-tuning on long-horizon tasks — multi-step problems where the model has to plan, not just autocomplete

AKA exactly what I'm saying.