That said, a lot of current agent workloads are I/O bound around external APIs. If 95% of the time is waiting on OpenAI or Anthropic, the scheduling model matters less than people think. The BEAM’s preemption and per process GC shine when you have real contention or CPU heavy work in the same runtime. Many teams quietly push embeddings, parsing, or model hosting to separate services anyway.

Hot code swapping is genuinely interesting in this context. Updating agent logic without dropping in flight sessions is non trivial on most mainstream stacks. In practice though, many startups are comfortable with draining connections behind a load balancer and calling it a day.

So my take is: if you actually need millions of concurrent, stateful, soft real time sessions with strong fault isolation, the BEAM is a very sane default. If you are mostly gluing API calls together for a few thousand users, the runtime differences are less decisive than the surrounding tooling and hiring pool.

> TypeScript/Node.js: Better concurrency story thanks to the event loop, but still fundamentally single-threaded. Worker threads exist but they're heavyweight OS threads, not 2KB processes. There's no preemptive scheduling: one CPU-bound operation blocks everything.

This cannot be a real protest: 100% of the time spent in agent frameworks is spent ... waiting for the agent to respond, or waiting for a tool call to execute. Almost no time is spent in the logic of the framework itself.

Even if you use heavyweight OS threads, I just don't believe this matters.

Now, the other points about hot code swapping ... so true, painfully obvious to those of us who have used Elixir or Erlang.

For instance, OpenClaw: how much easier would "in-place updating" be if the language runtime was just designed with the ability in mind in the first place.

Claude code already works as an agent that calls tools when necessary so it’s not clear how an abstraction helps here.

I have been really confused by langchain and related tech because they seem so bloated without offering me any advantages?

I genuinely would like to know what I’m missing.

The article touches very briefly on Phoenix LiveView and Websockets. I wrote about why chatbots hate page refresh[1], and it's not solved by just swapping to Websockets. By far the best mechanism is pub/sub, especially when you can get multi-user/multi-device, conversation hand-off, re-connection, history resumes, and token compaction basically for free from the transport.

1: https://zknill.io/posts/chatbots-worst-enemy-is-page-refresh...

Do I want this? If my request fails because the tool doesn't have a DB connection, I want the model to receive information about that error. If the LLM API returns an error because the conversation is too long, I want to run compacting or other context engineering strategies, I don't want to restart the process just to run into the same thing again. Am I misunderstanding Elixir's advantage here?

If an LLM returns garbage, restarting the process (agent) with the same prompt and temperature 0 yields the same garbage. An Erlang Supervisor restarts a process in a clean state. For an agent "clean state" = lost conversation context

We don't just need Supervision Trees, we need Semantic Supervision Trees that can change strategy on restart. BEAM doesn't give this out of the box, you still code it manually

What's that about years of experience? That's obsolete thinking!

> Your Agent Framework Is Just a Bad Clone of Elixir: Concurrency Lessons from Telecom to AI

Node is great, but scaling Elixir threads is more so.

Are you guys okay? WTF is going on with HN?

There’s one interesting detail about this blog though, you can see how the LLM-generated spam improves over the years as models get better.

Erlang didn't introduce the actor model, any more than Java introduced garbage collection. That model was developed by Hewitt et al. in the 70s, and the Scheme language was developed to investigate it (core insights: actors and lambdas boil down to essentially the same thing, you really don't need much language to support some really abstract concepts).

Erlang was a fantastic implementation of the actor model for an industrial application, and probably proved out the model's utility for large-scale "real" work more than anything else. That and it being fairly semantically close to Scheme are why I like it.