Yes, to a certain degree, although it does take initial effort to make it happen.

LLMs do somewhat okay job when you use them with Lisp, treating it just like any other PL, which is roughly the Unix/pipe model - batch-style. Agent spawns process -> reads stdout/stderr -> spawns next process. State lives in-between the calls and in files. Each tool invocation is stateless.

Things get far more interesting when you give an LLM a true Lisp REPL. LLM stops guessing and starts empirically analyzing current state of things and produces working solution faster, costing far less tokens. And you get to watch it solve things interactively, e.g. I often let AI poke through our UI (via Playwright-driven Clojurescript REPL), while monitoring situation in k8s - through nrepl port, connected to Clojure REPL.

Depends very much on your harness and effective use of tools. For vibe-coding, stick to Python.

It can get good with the right setup. I made Codex work with GToolkit (similarly underrepresented, but technically impressive and a seemingly good fit for LLMs), but it took a lot of tweaking of the project structure, extensive instructions in AGENTS.md, and some custom skills (some borrowed from Gt4Llm, the GT's built-in model harness). Out of the box, it burned tokens and took way too long to implement even basic things.

YMMV - as with everything LLM-related - but I think without a similar setup, an agent instructed to write CL/Clj/Scheme/Racket will have the same issues. It might be better in an established, large project - but starting from an empty Git repo, I suspect you'll have to fight your way to productivity.

I've found LLMs to be bad at balancing parenthesis. I've also found them to be less likely to hallucinate library types in dynamic languages, they tend to hallucinate arguments to library functions/methods instead.