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- Hacker News
- Perfect for when you want your data to be stolen programmatically.by chairhairair
- I think the line between regular LLM "endpoints" and agents/harnesses is going to become more and more blurry until it's a meaningless distinction.
When you're using ChatGPT/Claude/Gemini etc. you're basically already interacting with some backend harness with tools etc., not a raw LLM. Just give it a computer and be done with it.
I already find myself using Claude Code / Antigravity (via web) instead of Claude / Gemini, even for tasks unrelated to coding. Why use a limited version?
by brap - This idea of remotely hosting the agent harness is honestly backwards to what I need.
In so many cases, all the friction is about how to provision access to local data so the agent can work. So you started with the problem of how do I integrate an agent that is running locally with data that is hosted locally, and you have to deal with a bunch of security, data sensitivity and management issues around that. Now you moved the agent to a remote host - pretty much all your problems are worse: now I have a remote agent reaching into my infrastructure to deal with.
I'd much rather the inverse of this: let me run the agent local but provide secure remote hosted sandboxes. That actually solves a real problem because the sandbox running locally means breaking out of it directly intersects your local infra, whereas if it runs in a managed hosted environment I can leave the provisioning and management of that to someone else.
by zmmmmm - Instead of this push for more vendor lock-in, give us the reasoning tokens we pay for. Thanks.by monneyboi
- Kind of shocked nobody is calling out at the flag at the bottom of the announcement saying that it's not eligible for Zero Data Retention and it's currently pretty nebulous what the "Don't train on my conversations" toggle means, as the TOS classifies "conversations" as "user visible input and outputs" - says nothing about thinking, etc.
I suspect anything you make in these, the thinking traces or "safety evaluations" of the content allows whatever you build to become RL or eventual pre-training data.
by cududa - I've recently had great success running codex in a regular qemu VM and using codex remote control to talk to it from my phone.
Honestly works extremely well as a personal assistant.
I can see why turning it into an API makes sense, just be aware you might not need to lock yourself in if you can setup your own VMs.
- Buried in there, note you can opt to self-host your sandbox
https://developers.openai.com/api/docs/guides/agents-api/env...
That makes this much more enticing, and potentially eases transition between providers.
by 6thbit - I think we’re still figuring out the right abstraction for offering agents as a product.
- LLMs are a great foundation but building your own harness is a huge undertaking, a deep rabbit hole.
- There are harnesses available as open source libraries but that’s still coupled to an environment. Where does the state persist? Like maybe I’m a Cloudflare worker and don’t even have a file system.
Agent as a service like this lets you plug in the tools it needs to be whatever kind of agent you want. But they still get to encapsulate and continue to iterate on the really deep parts of the harness that all agents need like memory and context management.
That said, my money right now is not on the offerings from OpenAI and Anthropic because they’re stuck using their own proprietary frontier models and those aren’t actually the best choice for most agents right now. A competitor who is not an LLM lab gets their pick of the market at any given moment. Like you’d want to be using GLM 5.3 Flash right now for most things agentic.