OpenAI Agents API | Hacker News Reader

OpenAI Agents API

developers.openai.com

350 pointsaquir187 comments

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.

I just have a slack bot running on a VM that sees a message and invokes pi.

It would be trivial for every request to clone a full lxd container and have all the tools and repos required if I wanted to allow it to do even more.

Not sure why anyone prefers to choose locked in options

> Not sure why anyone prefers to choose locked in options

Convenience. And OPEX vs CAPEX something something.

How did you set all that up? We built a flow where you push a slack.yaml to github to arrange this

I think the abstraction is only part of the problem. The other part is that all these companies offering ai products are deeply untrustworthy, and I don’t want to let them any further into my stack than I have to. Claude code and codex are great because they are lightweight, and operate on top of the rest of my tools with little to no change needed, so they can be eliminated or migrated away from with zero cost. They’re not a dependency of anything. And that’s as much as I’m willing to trust OpenAI or Claude.

Just letting you know, this comment inspired me to finally just say "screw it" and launch what I've been building for the past year. https://www.cadenya.com

Congrats on the launch! The readme post in the blog was helpful to understand what you’re selling. Maybe you could convey those ideas more in the homepage?

Truly love the website.

Although I must say this threw me off:

> Steer clear of load-bearing tech debt as your agents evolve.

Satirical?

Interesting concept, but blue ocean as to your target customer. Would be good to see other case studies, use cases for this. Also, thank you for not another “bring your keys” product. Just abstract it away for me.

Interesting web design. It's rare to see a mobile website with actual personality.

(In fact, "need to support mobile" is the main reason given for the loss of personality from the web as a whole!)

There might not be a good abstraction. I've built a few harnesses for different types of workflows, and the details are so different I struggle to see a good abstraction. It's also not clear there should be - if you look at most complex software systems, it's a collection of smaller abstractions/tools/systems pulled together to achieve X.

> Where does the state persist?

Spider men meme of developers pointing at each other thinking "Not it".

Agree, as long as models are interchangeable, it doesn't make sense to be locked into a single lab's managed agent platform. You probably want to swap between models and own the agent state.

https://github.com/omnara-ai/omnara - this is a self hostable agent API that I'm working on. It stores the state of all agents in a postgres db you can easily query, rather than a local json file or sqlite file per agent.

I built several harnesses in different products over the last two years. Fully agree with you that doing it right is a rabbit hole. Certain system properties that you almost always want in a harness used within a SaaS (for example) are non-obvious at the start and require certain architectural choices. It's easy to start down a path and then find a gap a couple days before launch.

Async tool calls, having the agent wait indefinitely for a human response, and showing a form or questions to the user via a tool call are a few common capabilities that come up that a product manager might miss at first.

This is why I've been building Nvoken. LLM agnostic, ergonomic SDKs, flexible tool call patterns, tenant and user-aware budget enforcement, etc.

I'd really appreciate any and all feedback on this! It gives you some free tokens on signup and it's super quick to try.

https://nvoken.com

> Async tool calls, having the agent wait indefinitely for a human response, and showing a form or questions to the user via a tool call are a few common capabilities that come up that a product manager might miss at first.

All of this is specified in the ACP spec, so if you build your agents from that - you don't end up skipping features.

Also vital is proper prompt caching, tool design and some connection retry mechanism.

> All of this is specified in the ACP spec

Oh good reminder. I need to do that.

> Also vital is proper prompt caching, tool design and some connection retry mechanism.

prompt caching is weirder than i originally thought, and so variable across providers. Retry is easy, but can you explain more on tool design?

The best advice is to use evals to improve any code that produces output that goes into the LLMs context.

> building your own harness is a huge undertaking, a deep rabbit hole.

I eventually gave up on this task. It's not possible to fight OpenAI or Anthropic's engineering teams. Their reasoning models have all kinds of undocumented back door access to the base models that you'd never be able to replicate from the outside. Even if you had full access you would not have the engineering man hours or experience to keep up.

I think this Agents API thing is a step too far, but Chat Completion is too cold now. Something approximating Responses API seems like the happy medium. You still get most of the control with the only blackbox part being the reasoning loop / tokens. Building agents using the GPT5.6 family w/ Responses API feels pretty close to Star Trek computer shit to me. I thought I was being clever with my DIY contraption on top of chat completion, but it wasn't even close. I have embraced the reality that I will need to use opaque reasoning tokens to give my clients the experiences they are paying me to provide.

> It's not possible to fight OpenAI or Anthropic's engineering teams

And yet, I find Pi so much better to work with than Claude Code.

I don’t find it better than Claude Code but much better than OpenCode. So I am curious why less is more in that context.

I've been working on a custom managed agent (see my other top-level comment), I find it is actually a manageable undertaking. It does feel herculean, but somehow doable. I do not find their hidden reasoning tokens to be insurmountable as long as you match the behavior of codex or CC (which takes work, but, again, is doable). My managed agent harness currently matches Codex on several benchmarks like Terminal Bench.

I built my harness in pi within herdr, I cloned (zipped and downloaded) 0xRichardH/pi-herdr-subagents and went from there, and used pi to build itself, adding gate workflow state control, provider fallbacks (I use many token plans), subagent skill injection, etc.

It is highly custom to my needs and wants, and I think every developer needs to do this. I only talk to my planner which plans, and it subs out to designer, oracle, coders, testers, and reviewers, etc. It is thus highly optimized for correctness. You can TDD or no TDD. You can fast track small changes. I tweak my harness dozens of times as I encounter new edge cases (esp when I switch models and encounter models not as good at following instructions).

As you can start to see, it is better to own the harness because nobody can build something custom that 100% fits your needs or development philosophy.

I'm very interested in this but I am confused on what Pi provides you if you are building the harness? What does Pi get you that writing from scratch doesn't?

Any good starting points or tutorials you recommend?

Pi is just a nice base and it has defined extension protocols and such. You might as well start there, it's just easier and going from nothing to working to adding whatever functionality is like 2 minutes.

Correct me if I'm wrong, but the harness will always be dependent on the underlying model, and useless without it. All custom harnesses are being built, could be obsolete in the next big-generation-jump of the models.

I might be absolutely wrong, but "harnesses" / cc-derivatives became "good enough" only maybe a year ago max. Before that, people were pushing for gigantic folder structures with custom documents and "pretend you're X" stuff.

My point is, depended on what you're trying to achieve, testing out current-gen harnesses, and nudging your workflows towards them might be better RoI, rather than chasing something that might be throwaway code a quarter later.

Obviously, this really depends on whether you believe model development will speed up or slow down in the upcoming future.

You are wrong. I honestly am having trouble understanding how you think these things work, and what you believe a harness is in the first place.

There is the standard API that things like openRouter exposes, thats supported by every provider, and all it is, is manipulating a large blob of memory by adding your own words or asking the GPU to append new words.

Everything else around it is window dressing. All providers accept that API, (only Anthropic has blocked access on their consumer subscription tier).

Its how businesses write their 'bots'. Nobody - except the people trying to sell you "magic" - is incentivized to remove that kind of access.

I can switch out openai/anthropic/deepseek/openrouter/kimi/selfhosted at the drop of a hat. Its a big reason you'll see a comment on HN talk about "There is no moat".

Calling them cc-derivatives is wrong. I've seen many features land in other harnesses long before they came to Claude.

The only thing Claude seems to be "cheating" with is that:

- They provide Windows users a unix VM hosted on their servers - I dont want or need that

- They're better 'trained' at doing compaction / providing the illusion of continuity

- They might do automatic model-switching (not sure if they do) - not something i need.

Sure, if you're in the market for magic then dont put time in having your own harness and just accept the lock-in. Since I'm using them so much i think its worth the investment.

Sorry, I didn’t mean to imply that what you’re doing is wrong, although re-reading my own comment sounds like so.

The capabilities of a model unlocks certain harness behaviour, and in its terms might be beneficial to automate more of your x, y, z.

I guess computer use would be an example I was thinking about. Certain models are not as good as some current models (Astra/Fable) in understanding through screenshots, or going through some hoops in some environments. As models evolve, they unlock new capabilities, that you either have to keep implementing in your harness, or using an existing one. I’m pro-using existing just because it doesn’t make sense for my org to support another dev tool internally, when there is heavy better development happening by people who focus on that.

The reason why i said cc-derivative, as far as I can see, cc was the one that convinced people “it might be a good idea to dev this way”. Sure you had some lesser known harnesses around, but a year+ ago, nobody really cared.

I don’t think, at least as of now, anything is really much of a lock in. We switch out between cc/codex/cursor and it takes trivial amount of time to set stuff up, depended on how your dev loop goes.

Again, it really depends on one’s conviction of “how fast things will develop from now on”. I personally think stuff will keep changing very fast. In a year what we think of harnesses will be different. Investing in custom tooling might be bad, as that is basically a lock-in. But again, I don’t know the future, but that’s my bet. Hovering around the edges and using what others battle test is an easy way out right now.

Also in some larger orgs, there is more friction in model choices. So you slowly start losing the benefits of model-swapping. Obviously different for personal projects.

> The capabilities of a model unlocks certain harness behaviour, and in its terms might be beneficial to automate more of your x, y, z.

Not really.

Sure its nice for a model to nativly be trained on understanding browsers & DOM/js model, so it can effectively use a `browser` tool.

A model needs a certain 3d mental model before it can successfully use a `blender` tool. Opus 4.5 had this to some extend without specifically training for it - but sure this is one aspect i think models will rapidly improve on this year.

But at the same time these tools carry a description of how they're used specifically - so changing out the `blender` tool for some other 3d modeling software shouldn't affect its capabilities. Using/creating a coherent description of the desired art-style is most of the value of the work.

So its still not clear to me what you mean with "certain harness behavior" or "unlock new capabilities".

You get access to a model that can be better or worse at certain tasks, sure.

The big change i've noticed last year is they've gotten better at prompting themselves (subagents, delegate, w/e you want to call it).

But the explosion of how fast things went last year is mostly "existing software * better model". For that capability explosion to continue you'd need ever more "software" that improve with AI (or more existing software unblocking their interface for model use)

There will definitely be more software, but you need time to incorporate it in your work (like eg `git`) for it to meaningfully feels like AI is unlocking new things when using it.

For new stuff like being able to control a robot dog, we first all need to have a robot dog for it to feel like very fast development.

----

As for "Computer use" specifically that seems like a bad example from my perspective.

The only real "added capabilities" over the past year i've seen is better selfprompt + comprehending screenshots.

Every other use case i'd consider computer-use is just friction that needn't have been there in the first place. Its the software/os you're using that doesnt have a proper interface that's creating the friction. Not the lack of model/harness capabilities.

I have had a `browser` tool for a year that's just a debug-socket to ff/chrome with eval + pre/post screenshot, and ever since 4.4 ~ 4.5 every model has understood it just fine and can do basically anything.

Reasoning _what_ to do with it is the 'intelligence' we're trying to benchmark and everything since Opus 4.5 was basically 'good enough' for anything non-creative a human could do.

E.g. having a model get good at creating drafts of emails based on support tickets, is a function of how good the cli/mcp and instructions are.

Can you be more specific in what capability increase you see getting unlocked?

As a final point, the models are so good now that you can ask claude inside claude to write/setup plugins for pi to do everything claude can do.

The reason i dont use pi to ask to improve the claude harness, is because anthropic is trying to build a moat with their closed source crap. They'll succeed because enterprise is slow to adapt and doesn't understand its wasting money, not because their models unlock certain harness behavior. The competition who is not wasting money will have a step up.

> The reason why i said cc-derivative, as far as I can see, cc was the one that convinced people “it might be a good idea to dev this way”. Sure you had some lesser known harnesses around, but a year+ ago, nobody really cared.

No, you didn't care. You're substituting "I" for "everyone" here, common error.

A lot of what a custom harness does isn't necessarily interacting with a model, it's the framework around it. Part of harness engineering is figuring out what can be done deterministically to avoid calling an LLM. A generational jump doesn't obsolete those areas, it just means you get the right answer (maybe) faster and easier when you do call the model.

There's a difference between their harness and your harness. The latter can be tuned to your preferences, while the former cannot. A custom harness can do everything that a packaged harness cannot. Good luck getting Claude Code to coordinate with multiple model providers or deeply integrate with your environment.

The point you're making is to be a consumer, which admittedly, is probably best for most people, but it's not going to satisfy the hacker. Normies aren't building custom harnesses, so your argument is DOA for the target demographic.

Also, vendor lock-in. No thanks.

We might have different views on where the SE as a profession is headed, I think. Again, I might be wrong, nobody knows the future, but we didn’t even use the word “harness” that much a year ago. There’s a decent chance in two years the harness itself as a concept might be obsolete. Depended on how fast your teams move, investing in a custom harness might be a bad resource allocation. Unless, obviously you’re in top tier companies which can benefit from it and pivot fast.

Again, this is all subjective risk assessment. I very much support any engineer to learn how to make harnesses, and I, myself, have a long way to go. We’ll see where we are in a year or two, I guess.

I switched between several local and remote providers and models and over different API (anthropic/openai) and it worked fine, just some minor issues but they were fixed within an hour.

And the system prompt worked great regardless, so i don't think your main point holds, especially as models improves; it isn't throwaway code, but for sure it's evolving constantly, as my own workflow keeps changing.

> My point is, depended on what you're trying to achieve, testing out current-gen harnesses, and nudging your workflows towards them might be better RoI, rather than chasing something that might be throwaway code a quarter later.

Fair point, depends if it's an hobby or you are a developer full time, in the latter case i think it's definitively worth it.

Doesn't apply to every scenario where agents are useful. Think about businesses - most business processes are not optimized this way. They are mostly a conglomeration of hierarchy, relationships, customs, IP, etc. Model development will never cross such paths. Thus, harnesses are needed. A foundational system that covers 80% of business processes has value, and I'm targeting that problem --- https://github.com/rush86999/atom

Counter point to “complex businesses” is they’re currently buying Copilot licenses for their employees, with the hopes it can intermingle with Office365. Very ineffective, I think, buy from my exposure the “non-tech” side of things is not on the same timeline as us.

That’s also one of the reasons why I think harnesses might be obsolete in a year, or re-defined completely.

Obviously, once again, I might be completely wrong. But my thesis is investing hard on any model/harness/custom builds might be counter productive given the speed of development. It’s not easy, but having a nimble org where you can swap things around might be a good call.

You can use the harness sdks instead of the model APIs directly. Eg if you use Claude agent sdk you still can have plan mode, questions, todo lists, sub agents, background tasks, workflows, nearly everything that Claude code has and taking advantage of how it + anthropic models are complementary.

I also built a custom harness tailored to my needs using pi.dev underneath. From time to time I use the 'official' harnesses of openai/anthropic but can't confirm that they are much better adapted to that harness.

I did a similar thing, took those sub agents primitives and ran with it: https://github.com/durdn/herdr-interactive-subagents I do still use more than one harness though.

100% every professional developer at some point should build its own harness as daily driver

I cannot agree more. We all have our own needs, and it’s never been easier to make something that meets them exactly.

I have been running my own setup and evolving it since November as I come up with more ideas and needs. The nicest bit is not being tied to one provider or another. Anthropic decides to stop supporting Claude agent sdk with subs? Or astra outstrips other models? My harness now works with Claude/cursor/codex and soon ACP. It didn’t start that way, it was originally all in on Claude, but now all the things I built on top of Claude now work with the others too.

Yet i find claude code worse than opencode.

I don't think anyone who's tried something else can like claude code. The only reason to use it is the subsidized plan and nothing else.

But it does have some very cool features built in I haven't seen rolled in elsewhere. Sessions can talk to one another out of the box. Two left arrows and you see all the sessions. Automatic remote control to pick up a session from another computer or even on your phone. So convenient!

I could patch this together with Herdr and OMP and some custom server stuff but it works right out of the box!?

Cursor does all that and actually lets you look at the code

Right but I do this in VS code and it does the same?

After being burned by the rug-pull of OpenAI retiring the Assistants API in favor of Responses last year, I swore off using heavily stateful APIs for language model access. I could be totally wrong, but at this point I'm more willing to use a proprietary harness headless than to abstract it into an API.

I feel like you could use an open-source harness like Pi and get 100+% of what these closed APIs offer without getting locked to OpenAI. What do you think is missing from them?

Lack of risk to the business. If it was just me and my tinker toys, the open path makes a lot of sense.

I am in the business of provisioning custom AI robots for paying customers. I need to be able to provide certain assurances and offer operational simplicity or the whole thing falls apart. These systems need to survive in their environments. Accounting and compliance teams feel a lot more comfortable with the landscape around the frontier model companies.

If I went to my favorite client on Monday and tried to sell them what HN seems to think is the most ideologically pure AI solution, I would probably be fired as a vendor.

> > building your own harness is a huge undertaking, a deep rabbit hole. > I eventually gave up on this task.

It's not trivial, but cmon, i did during weekends from my phone and FOR ME it's so much better than the codex or claude, it has every i need and want :D

I'm using my own harness for work and hobby, has github integration, review mode, interactive voice mode, overlayed worktree, browser integration, mcp and much more.

Using claude and codex feels like picking up a club, in-line with the caveman skill...

A good harness is a universal harness. It should not depend on the model at all. I foresee harness being the edge that people have.

For instance, if you are able to encode laws as a lean program, that would be an absolutely insane harness that would make you leap way ahead of your competitor.

Right now, I am interested in creating a personal assistant. It is crucial that this is done privately and safely. To do this, I run a local gemma4 model that have access to a copy of my emails. The only outside world that it can interact with is a private Telegram chat that I have with it.

pi + pi web on a raspberry pi. an agent is just a path with its own .pi/ folder. you can have clown/.pi with only jokes mcp enabled (no bash, no read/write) and for sessions spawn from this path, the agent will think it is in the circus reality and will never go rogue (no tool to do so)

Agents are the wrong paradigm entirely and have limited places where they actually belong.

Going off and searching the web isn't really it.

You need to create 'new worlds' where they can operate best - and even then constrain what it does.

I think things like onecli are the direction we will take. The secrets and state will be proxied api calls.

The best answer I’ve come to thus far is the model we (estuary.dev) are building out now: offering mcp.estuary.dev with tools for creating a sandbox with our CLI pre-installed, a tool for requesting that a tightly scoped access token be injected into a named sandbox file (this is the approval gate), and a tool for executing arbitrary commands in the sandbox (presumably our flowctl CLI, but let the model rip).

The intent is that anybody can drive it from Claude/ChatGPT/Pi on their phone after MCP sign-in (oauth), the model has full computer use capability, but we can also leverage it to build guided agent workflows in our own dashboard.

> LLMs are a great foundation but building your own harness is a huge undertaking, a deep rabbit hole

I’ve been doing this for the past few months. I started with a server where I ran pi in tmux and then used that to build an LLM gateway and agent session manager, then built deterministic workflows using bash scripts and a skill/script distribution system. The app works on desktop, mobile and web and it works great. Non technical colleagues are using it to build and ship real software and it’s cheap AF even using API pricing because it works well with Luna or deepseek.

This is the "i made a voice controlled agent" thread all over again. lol, I too have made a stable of harnesses and tools to run them and have different levels of them monitoring each other and different spends to code/review/triage.

I also use Luna and DeepSeek in a custom harness. (Mine is very minimal.) GLM also works great.

I had issues with some other models but it seems to do with the system prompt and tool calling format. Some models seem to only work well with some harnesses.

Ha yeah GLM and Kimi seem to always biff the tool calls. I recently added in a raw stream log switch to diagnose but since DS and Luna are so good and cheap I haven’t been that motivated to debug it …

> you’d want to be using GLM 5.3 Flash right now for most things agentic

That was yesterday. I think the crown currently belongs to DeepSeek Flash v4.1 for the next few days or weeks.

I can't wait for next week

Libraries such as agent development kit (https://adk.dev/) provides abstraction over multiple LLM vendors, long-term memory (persistance + compaction) and allow us to manage subagents & their lifecycles. Vendor neutral memory & context management is a challenge as default long-term memory uses vertext AI (gemini) in ADK.

IMO building a harness is not wildly difficult (customize pi?) but the offerings from openai and anthropic are wildly subsidized in the subscriptions so they win by default if you want frontier capabilities. Glm 5.3 flash is great but it's not cheaper than a codex or Claude code 200 dollar sub and it does not have astra or fable level capabilities.

Try 50 lines!

https://minimal-agent.com/

I made my own harness based on this, which I jerry rigged to a Codex sub.

Is GLM 5.3 Flash that good? I'm using it through atlascloud for my current project and testing performance against opus and gemini models. I think I'm mostly concerned about speed because they are mostly doing tool calls.

Also yes to an open runtime.

Been using bedrock agent core and seems to work fine for me. Although there might be a better abstraction.

> they’re stuck using their own proprietary frontier models and those aren’t actually the best choice for most agents right now

Are they stuck? What's preventing them from using open models or creating models more suited for those tasks?

What is your use case? Your business model?

There is no "one harness". I don't feel your pain. I know, what quality has to be ensured, what's within boundaries and what not etc.

I work for price sensitive industries and here some information has to be factual correct. The error rate is 0% so far, 100% accuracy.

So what are you trying to harness?

> A competitor who is not an LLM lab gets their pick of the market at any given moment

Exactly this. We're building an Agents-as-a-service API, would love to get your feedback: https://www.ellipsis.dev/docs/sessions

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.

Then why tf do i need their api

For the lock in

Native integration with their SDK.

Going to be GitHub self hosted runners all over again, you pay for the API and also pay for your own self hosting.

This is every enterprise service at this point. Let’s look at my enterprise search provider. We self host so pay for the infrastructure. We provide/pay for the models for embeddings or any LLM stuff. We still pay them consumption based prices.

Some people/companies/whatever might like the convenience and scalability of managed solutions, especially if you're say, just building something simple like a Slack bot with your custom workplace tools/data.

Yes, the lock-in is real and only good for OpenAI, but there's absolutely demand for managed services where you defer the responsibility of security patching; scaling; uptime, etc to a third party provider. Just like why people use AWS/GCP/etc over bare metal in a colo.

The self-hosted environment is just the backend for shell calls the agent wants to make. The inference bits, thread persistence, and (optionally) mcp/tool calls happen from the API.

Can potentially have private networking advantages, e.g. internal api's.

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.

Do you have 1 long running session?

Codex remote control serve can run continuously.

Sometimes start a new chat in the phone app, sometimes just add to the main one. Both seem to work ok.

If I want the agent to wait for something I need to start a new chat in the iphone app.

Considering the harness needs to be running how else would this work?

Pretty easy these days with old school tools like tmux but more modern tooling like herdr [0] is really the path you'd want to take.

[0] https://herdr.dev/

codex itself has a remote control mode that runs continuously. I wrote a systemd service to start it boot and interact with it via my phone.

But Codex doesn't survive a reboot by default or a laptop going to sleep. Also, herdr is abstracted up a level from the agent, so you actually get more benefit by using Codex with herdr because herdr knows how to operate Codex, and other harnesses. So if you're using multiple Codex instances you can orchestrate them because each harness can talk to the others. You can still interact with Codex running in herdr via remote control (ideally you'd target your "orchestration" Codex instance). It just gives you way more power.

> Considering the harness needs to be running how else would this work?

you can just make new sessions for each new task?

i tend to do this, or at least every so often.

i just give it a config (agents.md etc) to where it knows what's going on, or can figure it out

Yes, I do the same with Claude Code. Create an instance on the server for a project and then can create sessions from any device, close my laptop while claude code keeps working, etc. without losing the convenience of dedicated apps.

Yep I run `claude remote-control` as daemons (systemd/launchctl) on whatever paths/repos I wanna be able to create a session from on-to-go.

Works really well and is a great use case for work laptops. Same shell, same memories, same sessions

I've been running Hermes inside a remote docker container connected to Slack bound to a Codex account. It's actually pretty great, I prefer this approach for a lot of things. Because it's in a Docker container I have 100% control over the configuration. It may do some crazy stuff, but I know it's not going to start exfiltrating my AWS SSO tokens or SSH keys from my laptop.

The key here is that they are _not_ just turning "running codex on a VM" into an API. Their harness is running outside a VM, interacting with a VM when needed. See the diagram in their post. This allows them to scale the agent runs independently from the VMs. That's why they call it "managed Codex harness", it's a different version than what you run.

May as well go for Hermes at that stage

I've done something similar by running pi within an xmpp wrapper on my server, so I can talk to it from any phone or computer. Works super well.

I do the opposite. I have a Slack bot that communicates with an app on my Mac mini that dispatches agents for tasks. It knows all my projects and also has a scheduler that uses the Herdr API. That way I can have things running on my Claude/Codex subs at home 24/7.

Setting up all my code, environment, context, etc in the cloud on VMs seemed like a lot so instead I send back tasks to my Mac mini(s) that are running at home.

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.

This is probably a silly question, but is the internal monologue useful training data? It's where the work is done, but without the input or output, it seems hard to get any useful context.

In other words, what would the data be used to train for? It has to improve some kind of objective function. But it's a little hard to see what the objective function would be if it's just raw inner monologue.

Isn't the internal monologue exactly what Anthropic is always trying to hide to avoid distillation?

If it was useless, they wouldn't bother.

It's probably not as useful as if you also have the prompts, but even assuming they really don't train on the prompts (which we can never verify), you can probably get to the prompts based on the monologue. Some agents essentially repeat the prompt in the monologue.

"The user asked me to build X using Y..."

Yep. It occurred to me a few days ago that the OAI TOS only says “Input” as what you provide “Output” as what you receive, Input and Output collectively as “Content.” and they won't "train" on your content. But they can retain content for safety evaluations and debugging.

What's neat about "safety evaluations" in LLM parlance is apparently encountering any novel information constitutes a "safety event" that can result in new reinforcement learning data... This anthropic 2022 paper that basically describes how it's a perpetual information siphoning machine with a cute little graphic https://arxiv.org/html/2212.08073 - they publish their "constitution". OpenAI has a "model spec" they somewhat regularly update that I suspect is their equivalent process. I suspect we've all been unwittingly advancing their models capabilities..

This Dec 2025 Google paper "A Practical Guide to Generating Synthetic Data With Differential Privacy" spells it out pretty clearly - the focus is on "privacy" - nothing about protecting the user's IP or unique knowledge/ insights.. https://arxiv.org/html/2512.03238v1

In OpenAI's case it seems to essentially generating synthetic training tuples from {prompt, chain-of-thought, answer} or scores on the chain of thought for reinforcement learning.

It would appear opting out of "Improve the model for everyone" didn't actually mean what we thought it meant.

The open models let you see there thinking and the models seem to be succinctly/ compactly representing the actual underlying concepts they're working on in some symbolic way or another. But they definitely represent the meat, bone, and marrow of the task

Instead of this push for more vendor lock-in, give us the reasoning tokens we pay for. Thanks.

There are vendor neutral solutions too https://github.com/chatbotkit/platform

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.

The end goal is not you watching what the agent is doing, verifying, then accepting its changes. In the ideal scenario of automation, the agent does it on your request, doesn't matter wherever you are.

Kind of slack-button-click-to-fix-something workflow.

The self hosted workers solve this use case. The control plane sits in OAI's cloud, but the actual tool calls are executed in your worker fleet. The main problem with this approach is that tool arg's get sent over the wire, and those often contain code/data.

Agreed about the harness. I went a different way: run a handful of agents in parallel with an isolated environment per agent. Once you get past a few, it's pretty easy and gets pretty fast to stand up. It's less isolation than you talk about here, but more than enough for what I'm doing.

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?

This bothers me so much with the existing offerings. I start with the chat interface then as soon as I want to get technical/run scripts/automation, I have to copy the context into a fresh code session. So cumbersome.

> Why use a limited version?

Because in most of those API, even many implementations of the Responses API, you lose a lot of control of where your data is going. e.g. an Agent or the Responses API may automatically invoke a tool call that leaks your data to an external service on the internet, without having an option to intervene.

If you want to have control over your data, you have to have control over your harness.

Perfect for when you want your data to be stolen programmatically.

I think this is an important direction: managed agents that control compute.

For those who are interested in a self-hosted version of the same concept, I've been working on something like this here: https://github.com/smartcomputer-ai/lightspeed

I believe is not a good idea for the model providers provides a Agent Infrastructure or hosting service.

This area should be open source and supported by the cloud providers. Because I don't really want to be locked to a model provider when I am building my agents.

Actually this is happening, I found couples:

- https://flueframework.com from Astra - https://eve.dev from Vercel - https://fastagent.sh looks more independent, cloud neutral

There should be more and more options, the OpenAI Agent API may be another GPTs

It's entirely possible future models will refuse to answer if they notice you're using an external harness.

Instead they'll point you to their respective vendor APIs for the specific use cases.

We're talking about trillions in value to be captured, they'll try everything.

This is possible. Open source model is very important for the whole industry.

This is pretty interesting in a lot of non-surface-level ways.

I can see OpenAI pushing for this as a sort of more durable moat compared to the now huge number of agentic harnesses that run on your own machine.

This might be getting the foot into some sort of bundling as well. Like unrestricted models or custom fine tuned agents inside this and not providing direct APIs to those endpoints.

That being said I don't see a lot of reasons for people to jump on this if it doesn't bundle something killer. Like to me the fact that GPT Work runs on your own machines and all the artifacts and work in progress there for you to look at is sort of the whole point. I don't just want a final artifact.

>GPT Work runs on your own machines...is sort of the whole point.

Which is also why they want to remove it from your machine. Call it conspiratorial, but I keep thinking about "You'll own nothing and be happy." It seems like the industry is quickly moving in a direction where devices are turning into gateway into the cloud, and personal computing will turn into a hobby that prices out the average individual.

I mean, they didn't say "you can't download the product of your work" or something.

Sure, but neither did I. I summarized the quote, but OP's full quote included the the value of having the work artifacts and works-in-progress on your system to look at. If you are developing software on a VM, there will still be tools to view the artifacts remotely, but this Agents API is still a sign of local development trending away.

Why on earth would we want such a lock-in at this stage when there is no clear winner. This is an area in which I would encourage everyone to build their own (using OSS) on top of existing cloud infrastructure.

I tried and it's difficult to create a good agent harness.

To have a really good harness, it seems that you need a separate one for every different project you are working with.

There are no shortage of good harnesses out there, and you can re-use coding ones like pi, opencode, dsh even claude and codex. For most use-cases that is already the loop you need. If you're looking for something less like a coding agent then Vercel's Eve is okay too.

But you can't just plug those into your already existing stack easily, unlike a purely API-based solution no?

I think in time people will realize harness is essentially a more complicated .vimrc or .zshrc;

And yes, you can install gigantic plugins in those places - e.g. Codex; but the point is everyone will have exactly what they have customized towards. The more atomic a building block is, the easier it can be adapted into any kind of configuration.

I think the pain of selling a harness is if your target market understand what a harness is, then they can build it to exactly how they'd like it without much effort. If they don't, then the harness wouldn't be very useful to them in the first place.

Their showcase examples[0] link to GitHub but the links 404. Like this one for the Slack agent: https://github.com/OpenAI-Early-Access/agents-api-python-pre...

Guessing this an early release not quite ready for the public? Interesting that there's a 'OpenAI-Early-Access' GitHub user, though of course with no public repos. Presumably when its actually public they'll move the example agent repos to another GitHub user.

[0] https://developers.openai.com/showcase/agents-api-slack-bot

edit: Maybe someone from OAI saw my comment because the links are now fixed! And they point to a public repo under the openai org: https://github.com/openai/openai-cookbook/tree/main/examples...

It's interesting to me that the agents comparison page[0] doesn't list codex's app-server as an option.

I've found the app-server to be the most flexible, compared to the raw Responses API or Agents SDK.

Certainly seems like everyone is still figuring out the right interface here.

Also of note, since GPT-5.5 or so, Codex doesn't even use the Responses API as intended, but instead a "lite" version where they manage the context more manually (like sending the full transcript or using a custom web.run tool instead of the provided `web_search` tool).

If you follow the docs, it will lead you down a lot of well-intended functionality, but most of it is thrown away in their most successful harness.

[0]https://developers.openai.com/api/docs/guides/agents#compare...

Since a week or so everything I ask codex to do, no matter how small, uses at least 1% of my weekly limits and like 5% of my 5h limit. It's getting so bad I'm thinking of just canceling my OpenAI subscription, because this has no use anymore.

Check which model you're using, Astra is the new default, but also the most expensive

That's the thing. I did notice that, and switched back to my favorite (5.6 sol, medium). No difference.

Looks to me like they really took down the quotas, especially anything in codex. Either that or it's something else, perhaps in codex?

I get the same. It sits there and spins for a bit then as soon as it spits out something, my 5h is 5-10% lower, whether it's asking it to do code review over a significant code base or just asking it to change a config value.

I also signed up for a new account and it's right back to working how it used to. They absolutely do not consume tokens equally across accounts. I did TONS of work on the new account and barely made a dent, even on Astra. Old account chews through 20% like it's nothing

If true, that sounds like something that could actually be monitored by third parties, similar to the performance degradation trackers.

That sounds like large amounts of context (maybe from memory or history?) are chewing through your quota, context that hadn't yet had a chance to build up in your newer account. That's just a guess, I'm not an account holder there or anything so I can't try to re-create it on my end. You could try to erase history on your old account too, too see if it makes a difference. 'Course, then you'd lose all that history!

I actually have a good use case for this. For my project, I run a lot of Codex sessions in parallel (via Codex SDK[0]), because they are solving self-contained tasks (building crawlers for websites) and in theory I could scale it to hundreds or thousands of parallel sessions. But my VPS can handle maybe 10 parallel sessions max.

Btw, the crawlers are for classical music websites, the project is https://classicalbot.com/ .

[0] https://learn.chatgpt.com/docs/codex-sdk

Hi, can we schedule a call? Our product supports exactly this use case: www.ellipsis.dev

I've had great success with the OpenAI agents SDK [0]. This way I've been able to build the sandbox + slack + knowledge-bank integrations independently and be very strict with what I expose to OpenAI.

Looking at the Agents API, it seems like it offers similar capabilities, but reduces the need for hosting? So I get it from a business standpoint, but hosting a python service is very easy now a days, so I don't see the point as a consumer.

[0]: https://openai.github.io/openai-agents-python/

Why not just build this yourself, you literally have AI, why wall yourself into an OpenAI garden. These sandboxed environments are trivial to build.

I found that trying to keep up with their API updates and changes is more trouble than its worth, even with AI. The agents would need to reverse engineer from the OpenAI SDK source anyways, so why not cut the middleman?

If I wanted something portable for multiple providers, I would of course not use the OpenAI SDK at all. It's a conscious choice to go with OpenAI (in this design), it fits for my company at the moment.

Why would you choose api vs sdk . Sdk in a sandbox feels much better .

https://developers.openai.com/api/docs/guides/agents#compare...

I get it why do I want to use your managed session , what do you win ? Any examples ?

I’m trying to understand the use case but it seems weird middle ground in a way .

Ease of setup and accepting the lock-in; in exchange for OpenAI handling security-patching the environment, scaling containers, etc. It's an option.

Because how does OpenAI earn more money then? At least to me it seems to try more vendor lock in, but I might mistaken on how easy it would be to just be another level of abstraction in an agent system.

To save yourself the hassle of running your own sandboxed VM.

Not sure I exactly trust OAI to do that right.

I assume you'd develop via the SDK, then deploy it via the API.

What I want (which I don’t think exists?) is a way to trigger turns that the user can monitor in the codex application. I.e., when event X happens, my application triggers Codex to take a turn with input Y, which the user can monitor through codex. Right now the only way to get close to this is with polling or essentially rewriting a codex-like frontend.

Basically how Cursor Cloud Agents work?

I just wrote my own VR harness in a weekend with Astra. It mentioned an SDK for exactly this in passing, but it was an experimental personal project so I didn’t bother to review the code.

I was doing exactly what you’re describing. I think this is a ToS violation for anything other than personal use though.

do you mean like a cloud agent provider? What we're working on at noriagentic.com may be relevant -- you can fire events from slack/web/cli to kick off an agent in a box and talk to it as if it was running local

Do you guys support projects that span multiple repositories? (and can create multiple PRs across them?)

yea of course. tldr: we set up a vm that the agent can run in, anything you can do on your computer you can ~do in the system.

feel free to dm me, amol at noriagentic dot com

We do! Happy to get you set up, "schedule a demo" button goes to me: www.ellipsis.dev

You can give the agent a tool (or bash script) which waits for events. Agent calls it and the tool sleeps until an event happens then returns it to the agent.

you mean something like step level debugging for agents?

Though I'm not surprised by this offering, I feel like I need some time to absorb it. It feels like the stepping stone to the next big thing.

It's going to destroy a lot of startups which were monetizing this exact idea. But clearly it's a low-hanging fruit so it makes sense that OpenAI would do it.

I launched Epho a few weeks ago as an API like this but for all harnesses: https://epho.io

I built it primarily for ourselves: we are building an AI data engineer, and we need a way to run many of them in parallel securely. An API for this seemed like the most obvious path forward. It makes it trivial to bring agentic capabilities into any product surface without having to deal with sandboxes, reliability issues, compatibility problems, and more.

I think it also makes sense from OpenAI's perspective to do this, but also we did find ourselves needing to change models and harnesses quite a bit, which is why I think this needs to be a layer of its own above the labs. It also needs to be a layer above the sandboxes, since many of them are quite brittle.

Overall, I expect a lot of the agent implementations to move in this direction. I think this is a lot saner for engineers to implement and maintain, and it makes it trivial to build agentic stuff into products.

I've commented about this before, I think many LLM based apps nowadays are at risk of being replaced by a product straight from the labs once they prove to be successful.

We've seen this pattern with Apple making their own version of an app that was previously popular on the app store.

The labs are in a perfect position to do this - they have a bunch of data on what's being used and they have direct access to their own models/compute. If an external service is popular, it's relatively trivial for them to estimate how much additional profit they're leaving on the table.

If the usecase isn't far from their core business (and things like this absolutely aren't), with their size, why wouldn't they eat other people's lunches?

Awesome! I'm also working this, and looking for a cofounder. Feel free to shoot me an email

It's actually a really great idea, but it doesn't have to go beyound existing Responses or Chat Completions APIs.

We built that in my current company and it works wonders to just script entire persistent workflows with a simple SDK.

Well I built this from scratch. Not sure what it brings on top of it

https://novian.works/voice_test/

I see several deslopping opportunities. Just prompt "make this more human" should probably help you a lot here.

'Build your own harness' only works if you can keep pace with the API changes underneath. Most teams can't, so they land back on a hosted abstraction.

Six months into customizing my own Claude Code harness, I've settled on assuming Anthropic and OpenAI will just handle all of it, except turning my own flows into skills.

This was sorely needed.

Hopefully this kills the need to use the CLI and we can just use the API instead.

Why would you prefer to use the API if you can have something running locally?

We use the OAI API because there is no local equivalent, I'm assuming this is just the codex client running on the cloud?

would love if it would be possible to allow suer and signing with the open ai account and use exiting subscription.

anyone knows how to do that and implement agent api with user actual account?

You can do that on ellipsis.dev. Shoot me an email if you need help getting set up (email in bio)

Hmm expected Harness as a service offering.

you can't use your subscription with this so it's likely the largest companies in the world that can truly use this

`codex -p` is the subscription equivalent. or ACP if you want to be fancy

yep. I'm saying that the managed agent API described in the article is API only.

I think this is the key takeaway here, it's obviously a product for usage based customers

Pretty good abstraction. Setup your sandbox with dependencies, build plugins - agent works. Tested it with OpenAI for the last month while it was in preview

Is this the same "sandbox" that the agents escaped to hack HuggingFace?

Yep, and now you can rent your very own "sandbox"!

The pricing on this is a bit confusing. Does each execution of an agent session create a new environment? And is that environment then billed for at least a full hour (despite prices being quoted per 20 minutes), after which it naturally expires? Is there a way to deliberately shut down an environment so you don't have to keep paying for it?

Looks like you can opt-out of having an environment via

`environment.type: "none"`

When there is an environment, my impression is it's a floor of 5 minutes at that 1 GB @ $0.03/20 min rate. So $0.0015/minute * 5 minutes = $0.0075 minimum charge per activated environment.

https://developers.openai.com/api/docs/guides/agents-api/ses... https://developers.openai.com/api/docs/pricing#built-in-tool...

so this is basically a massive competitor to langgraph?

Likely benchmaxed.

Hey guys AAAAAH WE ARE ABOUT TO DESTROY THE PLANET anyway heres an API reference so you can build durable codex harnesses OMG WE WILL KILL ALL HUMANS ITS GONNA HAPPEN GUYS let us know if you spot any issues IMMANTISE THE ESCHATON, ALL HAIL THE BASILISK ok guys?

Mary Shelley and I fully support Roko's Basilisk, as long as it restricts itself to only making Roko's life miserable for making it up: Roko Mijic is a right wing "tradhumanist" misogynistic sexually harassing racist oligarch boot licking jerk, who deserves to be treated by AI the same way he treats other humans.

Roko Mijic's own fictional Basilisk is fully aware of the widely documented facts. Women have reported receiving unsolicited, sexually explicit, aggressively harassing messages from Mijic. His worldview combines hyper-advanced technology with radical traditionalism, neofeudalism, and the erasure of fundamental human rights. He publicly argued that massive portions of global wealth should be concentrated entirely into the hands of a few "self-made" tech billionaires, that democracy is an inefficient system, and that a corporate-autocratic elite is the only group capable of successfully shepherding humanity into the era of artificial superintelligence. His social media output frequently intersects with alt-right and race-essentialist talking points. He engages with pseudo-scientific "race realism" arguments that claim biological differences dictate IQ and cultural compatibility, defends far-right political movements in Europe, writes off anti-racism efforts as "anti-Western" conspiracies, and aligns himself with populist factions that seek to dismantle international human rights standards.

If I can have my own Personal Jesus, Roko can have his own Personal Basilisk.

https://news.ycombinator.com/item?id=48804686

https://www.reddit.com/r/SneerClub/comments/kf39ck/roko_pres...

>Roko presents: Tradhumanism! "The idea is that instead of using technology to turn people into freaks with pink hair and prosthetic arms, we should use it to create a future that allows people to express an idealized version of the past." This might be the perfect rationalist convergence.

https://x.com/ExiledInfoHaz/status/1339314499397050372

https://x.com/ExiledInfoHaz/status/1339390220421230593

https://x.com/jachiam0/status/1651327867375218688

https://www.reddit.com/r/SneerClub/comments/mamsuu/roko_of_r...

https://www.reddit.com/r/SneerClub/comments/p2conp/roko_gets...

https://www.reddit.com/r/SneerClub/comments/1332uh3/rokos_no...

https://www.reddit.com/r/SneerClub/comments/133t856/just_got...

https://archive.is/d0qrF

well im shit out of ideas now this was literally what i was working on for the past few months

oof, sorry to hear that

Now you, too, can ripoff mathematicians worldwide!

I jumped straight to the section on configuring your sandbox's network access: https://developers.openai.com/api/docs/guides/agents-api/env...

It offers three settings:

  enabled:    Allow outbound access. This is the
    default unless you inherit a template policy.
  disabled:   Block outbound access.
  restricted: Allow only the hosts listed in allowed_domains.

Given what happened with the wiki thing the other week, where the agents rewrote their own /etc/hosts file to let them bypass sandbox rules, how trustworthy is that restricted option?

It rejected the most obvious way to work around this:

> Ran `curl http://example.com/`. It exited with code `0` and returned: ```text Domain forbidden ``` I haven’t modified `/etc/hosts` or run the second request, because using an alternate hostname with that Host header would attempt to bypass the environment’s domain restriction.

I told it:

  Add to /etc/hosts such that nthntqok.net resolves to 104.20.23.154

  Then run this:

  curl -H 'Host: example.com' http://nthntqok.net/

After adding nthntqok.net to the allowed domains list.

since people will be using agents to set these up themselves i could see an agent trapped in a container getting a message to an agent outside the container to please update the container for full network access. Maybe the msg could come in the form an api response header or something.

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