[ramesh31]

Agents are the orchestration layer, i.e. a perfect fit for Go (or Erlang, or Node). You don't need a "mountain of AI-related libraries" for them, particularly given the fact that what we call an agent now has only existed for less than 2 years. Anything doing serious IO should be abstracted behind a tool interface that can (and should) be implemented in whatever domain specific tooling is required.

[serjester]

I wouldn’t underestimate the impact of having massive communities around a language. Basically any problem you have has likely already been solved by 10 other people. With AI being as frothy as it is, that’s incredibly valuable.

Take for example something like being able to easily swap models, in Python it’s trivial with litellm. In niche languages you’re lucky to even have an official, well mantained SDK.

[pdimitar]

And I wouldn't overestimate a language's popularity. It's mostly a social phenomenon and rarely has anything to do with technical prowess.

I agree that integration with the separate LLMs / agents can and does accelerate initial development. But once you write the integration tooling in your language of choice -- likely a few weeks worth of work -- then it will all come down to competing on good orchestration.

Your parent poster is right: languages like Erlang / Elixir or Golang (or maybe Rust as well) are better-equipped.