hckrnws

Based on the docs and API surface, I think the filesystem abstraction is probably copy-on-mount backed by object storage.

I suspect it works as follows: when a task starts, filesystem contents sync down from S3/R2/GCS to a local directory, which gets bind-mounted into the container. The agent reads and writes normally - no FUSE, no network round-trips per file op. On task completion or explicit sync, changes flush back to object storage. The presigned URL support for upload/download is the giveaway that object storage is the source of truth.

This makes way more sense than FUSE for agent workloads. Agents do thousands of small reads (find, grep, git status) that would each be a network call with FUSE. With copy-on-mount it's all local disk speed after initial sync.

Cross-task sharing falls out naturally - two tasks mounting the same filesystem ID just means two containers syncing from the same S3 prefix. Probably last-write-wins rather than distributed locking, which is fine since agents rarely have concurrent writes to the same file.

That's a good analysis:) We want to go with FUSE but the performance overhead, especially with multiple calls to use files, is a constraint

sprites.dev / fly.io has publicly said they are using a variant of JuiceFS for the object-storage-to-VM-filesystem stuff, it's cool tech.

* https://fly.io/blog/design-and-implementation/ * https://juicefs.com

Really interesting platform — the decoupled filesystem model makes a lot of sense for long-running agents.

One area I'd love to understand better: inter-agent communication and auditability. When multiple agents share the same filesystem (e.g., a coordinator agent and several sub-agents), how is message passing or state handoff handled? Is it purely file-based (agents read/write to agreed-upon paths), or is there a more structured IPC mechanism?

More importantly, from an audit perspective: is there a way to replay or inspect the full sequence of reads/writes and agent messages across a multi-agent task? For production use cases (document processing, internal tooling), being able to trace why an agent made a decision — and which files it read at that moment — feels like a hard requirement. Curious whether this is on the roadmap or expected to be handled at the application layer.

We do have a communication protocol between the agents, but it's quite rudimentary. It allows sending messages and creating tasks for other agents. The state module for a particular task is accessible by other agents as well.

We're experimenting with multi-agent systems to figure out what the right API would be for agent to agent communication. We've found Claude Code's Team feature is a good starting point for the abstraction, but we think there's better abstractions and are creating the primitives to allow people do create custom definitions to explore.

Re: audit perspective. We have something we've been working on that we're excited to share soon which I think you'll like:)

Holy emdash, you real?

Ha, real human here! I'm a Chinese developer. I composed the question myself in Chinese and had AI translate it to English, hence the em dashes. The underlying curiosity about inter-agent auditability is genuinely mine though.

A polite request: English and Chinese are very different languages, asking AI to "translate" your thoughts sanitizes what you have to say, your words lose all of your personality -- a great shame. Participation from non-English speakers is wonderful but rather than use AI to "translate", using a literal translator (e.g: Google Translate, DeepL) will ensure we get to hear what you have to say, not what AI thinks you want to say.

The English that English speakers post on Hacker News is often grammatically incorrect, clumsy, misspelled, and that's okay, good, even. We want to hear from you!

(My preference is for translators to include both the original Chinese words, and the English translation because it means your fellow Chinese speakers get to read your exact words, but of course that is personal preference :)).

That's actually a quite rude and condescending request.

How so?

Congrats on launch! As the agent cli’s and sdk’s were built for local use, there’s a ton of this infra work to run these agents in production. Genuinely excited for this space to mature.

I have been building an OSS self-hostable agent infra suite at https://ash-cloud.ai

Happy to trade notes sometime!

This is really interesting, congrats on the launch. The use case I’m trying to solve for is building a coding agent platform that reliably sets up our development stack well. Few questions! In my case, I’m trying to build a one-shot coding agent platform that nicely spins up a docker-in-docker Supabase environment, runs a NextJS app, and durably listens to CI and iterates.

1) Can I use this with my ChatGPT pro or Claude max subscription? 2)

We don't support docker-in-docker yet, but that's something on our short term roadmap. We have the need for this ourselves! For now, you could call a different service to spin up your sandbox with the image of your codebase. Not ideal, but this is what we do now.

Yes, you can use your own subscriptions as long as you follow their guidelines

Dagger (dagger.io) has its own container execution stack, and supports dagger-in-dagger natively, with logical scoping, and without depth limit. Would love to show you both a demo, if you're interested!

(Disclaimer, I'm the CEO of Dagger)

I founded Docker, and lack of proper nesting support was always a pet pieve of mine. I couldn't fix it in Docker, so I fixed it in Dagger instead :)

Hey I'm working on this problem (also a YC company but it's FOSS). It's a Dind approach https://coasts.dev/, I wonder if this works for your setup.

Cool project!

Thanks! Still ironing out early kinks but I have a couple of friends using it. It’s been a joy to work on.

> We built Terminal Use to make it easier to deploy agents that work in a sandboxed environment and need filesystems to do work.

When I read this, I think of Fly.io's sprites.dev. Is that reasonable, or do you consider this product to be in a different space? If the latter, can you ELI5?

We overlap at the sandbox layer, but we're focused more on the layer above that: packaging agent code + deploying/versioning it, managing tasks over time, handling message persistence, and attaching durable workspaces to those tasks.

The version pinning approach for existing tasks is a pattern I've found really useful in practice. When you're building document processing workflows (transcripts, reports, etc.) the ability to iterate on your agent logic without retroactively breaking existing user sessions is underrated.

One question: for the "existing tasks stay on old version" case, do you support any kind of manual migration trigger? E.g. if I fix a genuine bug in how I'm parsing a document, I might want to re-run the agent on specific old workspaces with the new version, rather than waiting for users to start new tasks.

Good question.

> for the "existing tasks stay on old version" case, do you support any kind of manual migration trigger

Yes, we support manually migrating tasks using "tu tasks migrate".

> if I fix a genuine bug in how I'm parsing a document, I might want to re-run the agent on specific old workspaces with the new version, rather than waiting for users to start new tasks

In this case the better pattern is to create new tasks against those old workspaces on the fixed version. You could do this on behalf of your users.

I'm currently using them to build an AI agent similar to lovable/replit-esq in tech stack and it works.

I started by managing the claude agent sdk myself in a daytona container, and it was a lot more challenging than I thought. The agent kept crashing in streaming mode and there was no thread crash, so it was hard to debug, esp in a cloud container like Daytona. I also realized that I needed to implement my own session management system + my own database if I wanna save the chat and on top of that streaming so the messages come out in real time. AND I need to manage my own container janitor/heart beat system so that un-used containers don't just sit there, but I also don't want them to go cold immediately after each message since cold start takes a bit.. They all seem simple but at each step there are some edge cases. I ended up vibe coding most all of that, but it was just quite fragile. For those who hasn't tried the agent sdks, it feels like it's clearly designed to be ran on a client computers with permanent storage + lots of ram than microVMs. Which was not what I expected.

After that, I tried to find some managed option. Starting with blackbox ai because I saw a vercel tweet about them, and for some reason I just couldn't get their agent API stuff to work AT ALL?... I'm curious as to if it's actually working for anyone. Then I tried sandbox dev, which doesn't store container/sessions/storage stuff out of the box for you, so it wasn't much better than doing the daytona container myself. And then I tried terminaluse, and it worked better than I expected given all of the other stuff that I tried.

So at the end of the day, it's kind of like a managed cloud services that does agent chat history, session recovery, streaming + a CLI that makes it easy for my own codex to debug/deploy + file system sync. From what I can tell, there isn't anyone else that can do all that and I'm pretty pleased with using them.

We've been running filesystem-based agents internally at heyvid for about six months now, and the deployment story has honestly been the messiest part. You end up hand-rolling so much scaffolding that's not your core product. The 'Vercel for agents' framing clicks immediately — I remember when Vercel did the same thing for frontend and it just removed a whole category of yak-shaving. Curious how Terminal Use handles state persistence across runs; that's been our biggest headache when agents need to resume mid-task.

The filesystem-as-first-class-primitive approach is interesting. I've been building MCP servers (Model Context Protocol) for infrastructure management — letting AI assistants interact with Proxmox clusters, ClickHouse databases, etc. — and the hardest part isn't the agent logic itself, it's the lifecycle around it: state persistence, deployment, and making sure the agent can't accidentally destroy things.

Your two-step deployment (config.yaml + Dockerfile) with separate storage looks like it solves the iteration speed problem well. One thing I've found critical in practice: for agents that touch real infrastructure, you need explicit confirmation gates on destructive operations. Does Terminal Use have any built-in patterns for that, or is it left to the agent implementation?

There clearly needs to be something in this space, but I can't imagine the world standardizing on a closed source system for this infra.

I know OSS business models are rough, but someone is going to solve this in open source and I think that is what will achieve traction.

Yep. And there will be 50 clones on GitHub by end of week. It’s just how it is now.

have you guys found any of the existing nfs tools helpful (archil, daytona volumes, ...) or did you have to roll your own? i guess i have the same question for checkpointing/retrying too. it feels like the market of tools is very up in the air right now.

howdy! two things on the archil front:

1. we're not NFS, we wrote our own protocol to get much better performance

2. we're planning on coming out with native branching this month, which should make these kinds of workloads much easier to build!

Yep, this whole area still feels pretty unsettled. The thing we've become convinced of is that workspace state needs to be a first-class product primitive instead of something tied to one sandbox. That's why we model filesystems separately from tasks and focus on durable mount/sync semantics.

We're currently rolling our own but we've been meaning to experiment with other tools.

I'm using Dagger to checkpoint and all the fun stuff that can come after

Im curious if you guys are seeing rate limiting issues. Agents sharing api keys tend to be retry storm monsters. I wonder how agent companies will address

When building, did you not have the thought or feeling that you would prefer the actual Claude Code and Codex harness to run, rather than just the SDKs also for your Agents?

You can use the default Claude Code harness with Claude Agent SDK (just set the prompt preset to claude code). Same with Codex.

Would this yield exactly the same behavior when thinking about sub-agents and all this functionality?

how does it compare to https://shellbox.dev? (and others like exe.dev, sprites.dev, and blaxel.ai)

We're trying to be a bit more opinionated one layer up: deployable agent runtimes with first-class tasks, persistent /workspace, and rollout/ops primitives like versions, rollback, logs, and secrets.

For example we make it easy to have automatic deployments from your github ci (using our cli), and you can monitor and manage all your deployments in our platform, along with logs, conversation transcripts etc.

I'd think of us more of the deployment, monitoring and storage layer rather than just the compute runtime.

Hmm.. so this is not the same category with computer use or browser use. I love the idea. Well defined and controlled sandbox is really useful. Off topic but I’m disappointed by computer use and browser use when I tried three months ago. They couldn’t complete many basic tasks. Especially browser use, it easily failed slightly unorthodox website. It can’t find select box implemented by div, stacks in infinite loop when the submit button is disabled, and it even failed to complete the demo in its own readme! I’m okay with open source projects a bit buggy, but a VC funded company, which already has the fancy landing page, provides the service to big corps, and offers paid plans, should at least make sure the demo works.

is this a replacement to langgraph?

Depends on your agent. We haven't used langgraph, but I'd think it's probably the best solution to deploy langchain agents. We're SDK agnostic. We're like langgraph, but for agents that works in a sandbox and needs access to a filesystem to do work.

ok. how is this different from openclaw?

Can you explain why everyone thinks we should use new tools to deploy agents instead of our existing infra?

eg. I already run Kubernetes

I think there are some primitives for agents that need to be built out for better security and being able to reason about them.

Agents run on infra, they have network connectivity, they have ACLs and permissions that let them read+write+execute on resources, they can interact with other agents.

To manage them from both an infra and security perspective, we can use the existing underlying primitives, but it's also useful to build abstractions around them for management, kind of like how microservices encapsulate compute+storage+network together.

I think of agents as basically microservices that can act in non-deterministic ways, and the potential "blast radius" of their actions is very wide. So you need to be able to map what an agent can do, and it's much easier to do that if there are abstractions or automatic groupings instead of doing this all ourselves.

The "non-deterministic microservices" framing is exactly right and I think most infra teams underestimate how much that changes things. With a normal service, you can map inputs to expected outputs and write tests. With agents, the blast radius is probabilistic and context-dependent.

The monitoring problem alone is closer to fraud detection than traditional APM. You're not looking for "is this thing up," you're looking for "is this thing subtly wrong in a way that compounds over the next 10 steps."

As a psychiatrist, this problem reminds me of something we studied for a long time. Patients get worse in areas we are not measuring, but the numbers we record still look normal. We learned that checking results catches things that checking process cannot catch.

I'd argue it's both. You also want to know when your agent has collapsed and is burning tokens and your budget.

Right, those abstractions and controls already exist in the Kubernetes ecosystem. I can use one set of abstractions for everything, as opposed to having something separate for agents. They are not that different, the tooling I have covers it. There are also CRDs and operators to extend for a more DSL like experience.

tl;dr, I don't think the shovel analogy holds up for most of the Ai submissions and products we see here.

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I think people pick new tooling not because k8s lacks horsepower, but because running per-user filesystem-backed agents on k8s forces you to build and maintain a surprising amount of glue code. Newer platforms put versioned mounts, local-first dev cycles, secure ephemeral runtimes, and opinionated deployment so teams can focus on agent logic instead of writing Helm charts and CSI gymnastics.

If you repurpose k8s with ephemeral volumes or emptyDir, a sidecar, you'll likely get predictable ops and avoid vendor lock-in. Expect more operator work, fragile debugging across PVCs and sidecars, and the need to invest in local emulation or a Firecracker or gVisor sandbox if you want anything like laptop parity.

Unrelated but your comments on https://news.ycombinator.com/item?id=44736176 related to the Terminal agents coding craze have helped me feel less crazy. People using GitHub Copilot CLI and Claude Code, they either never review the code or end up opening up an IDE to review the code, and I'm sitting here like, why don't you use the terminal in your favorite IDE? You're using a Terminal as a chat interface, so why not just use a chat interface? Or use the terminal in VS Code which actually now integrates very well with Claude Code and GitHub Copilot CLI so you can see what's going on across the many files this thing is editing?

The hype is so large with the CLI coding tools I got FOMO, but as you were saying in that thread, I see no tangible improvement to the value I get out of AI coding tools by using the CLI alone. I use the CLI in VS Code, and I use the chat panel, and the only thing that seems to actually make a difference is the "context engineering" stuff of custom instructions, agent skills, prompt files, hooks, custom agents, all that stuff, which works no matter which interface you use to kick off your AI coding instructions.

Would be curious to hear your thoughts on the topic all these months later.

Glad to find comradery! I've started the CLI interface to my custom agent since lol

The reasons are (1) it's faster to do admin work like naming or deleting old sessions (2) I have not gotten the remote setup to work yet (haven't tried) but I do want to use it somewhere

But yeah, it's gotten worse, the latest I recall is a new diff viewer for AI in the terminal (I already have git and lazygit)

It's hilarious to me how we are recreating decades of IDE advancements such that they work on the terminal, only for us to end up with what is essentially an IDE.

I was doing that with (neo)vim and reached the point that I wanted to stop having to maintain a sorta-IDE. I'm now doing the same with agents (custom vscode extension), but I find this different for a number of reasons, primarily that I don't want Big Ai deciding how I can interact with and use Ai.

One thing I took from ATProto is a strong belief that user agency and choice are the penultimate design criteria. To those ends, I think that any agentic tooling needs to support the majority of users' choice about how to interact with it (SDK, API, CLI, TUI, IDE, and Web). My custom agent is headed that way anyhow, because there are times where I do want to reach for one of them, and it's easier to make it so with agents working on their own codebase (minus vscode because the testing/feedback I haven't figured out yet)

Honest answer: the problems start when you're running 50+ agents across 3 different model providers and the failure modes aren't "pod crashed" anymore. They're "model returned confidently wrong output and the next 4 steps ran on garbage."

K8s is great at keeping things alive. It's not built to reason about whether the thing that's alive is actually working correctly. Agent infra needs to handle rollback at the logic level, not just the container level.

Yup! And this is a genuinely hard problem when you try to apply agents to domains other than coding. With coding, you can easily rollback. But in other domains, you take action in the real world and that's not easy to rollback.

We're thinking a lot about how we could provide a "Convex" like experience where we guide your coding agents to set up your agents in a way that maximizes the ability to rollback. For example, instead of continuously taking action, it's better that agents gather all required context, do the work needed to make a decision (research, synthesize, etc.), and then only take action in the real world at the end. If an agent did bad work, then this makes it easy to rollback to the point where the agent gathered all the context, correct it's instructions, and try again

The separation of "gather context → synthesize → act" is a pattern we've found critical in production agent systems too. In LangGraph, we model this as explicit checkpoint nodes — the agent can't proceed to the action phase without passing through a validation gate.

One thing we've run into: the filesystem abstraction works well for code artifacts, but when agents interact with external services (APIs, databases), you need a separate "action journal" that logs intended mutations before executing them. This gives you rollback even for side effects that aren't file-based.

The harder unsolved problem is multi-agent state coordination. When Agent A writes to a shared workspace and Agent B reads it, the filesystem gives you structural persistence but not semantic ordering. Have you thought about adding lightweight event ordering (like a simple monotonic sequence) to the filesystem layer? Without it, agents can read stale state even when files are fresh.

Congrats on the launch — the decoupled storage model is the right primitive.

Given what OP describes

> Our biggest pain point with hosting agents was that you'd need to stitch together multiple pieces: packaging your agent, running it in a sandbox, streaming messages back to users, persisting state across turns, and managing getting files to and from the agent workspace.

The k8s ecosystem already handles most this and your agent framework the agent specifics. What you are talking about is valid, though a different axis imo. Quality and guardrails are important, but not discussed by OP.

I think Kubernetes is a good candidate to run these sandboxes. It is just that you have to do a lot of annotations, node group management, pod security policies, etc., to name a few. Apply the principle of least privilege for access to mitigate risk.

I think Kata containers with Kubernetes is an even better sandboxing option for these agents to run remotely.

Shameless plugin here but we at Adaptive [1] do something similar.

[1] https://adaptive.live

We already do those things with k8s, so it's not an issue

The permissions issues you mention are handled by SA/WIF and the ADK framework.

Same question to OP, why do you think I need a special tool for this?

We have also built something custom ourselves (with modal.com serverless containers), running thousands of on-demand coding agents each day and already the assumptions that Terminal Use is making (about using the file system and coding agent support) would not work for our use case.

Curious to hear why we wouldn't work! I'd love to understand what assumptions we're making that won't work for your use case, and what we could work to improve on

It seems like so many of the AI "solutions" are hallucinating the problems. I either don't have them, because I use better AI frameworks, or I have tools at hand that solve them nicely.

We don't need to rebuild everything just for agents, except that people think they can make money by doing so. YC has disappointed me of late with the lack of diversity in their companies. I suspect the change in leadership is central to this.

At least on K8s you can control the network policy. That's the harder problem to solve. I suspect we'll see a lot of exfiltration via prompt injection in the next few years.

good point! programmable network policy and a gateway to prevent secret exfiltration are on the roadmap.

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This is what we see! We want to make it very easy to be able to granularly manage your agents (in terms of files they have access to, env var values, network policy, etc.) on a per-task basis.

With regards to permissions, mileage varies based on SDK. Some have very granular hooks and permission protocols (Claude Agent SDK stands out in particular) while for others, you need a layer above it since it doesn't come out of the box.

There are companies that solve the pain of authn/z for agents and we've been playing with them to see how we could complement them. In general, we do think it's valuable to be provide this at the infra level as well rather than just the application level since the infra layer is the source of truth of what calls were made / what were blocked, etc.

K8s gives you orchestration of Docker containers. I don’t think it handles the container boundary any more than Docker does.

I don’t think it should be assumed to give network isolation, unless you’re also using extensions and something like Cilium for that purpose. I don’t think it’s the right primitive for agent sandboxes, or other kinds of agent infra.

(Obviously, you could still run a custom runtime inside k8s pods, or something like GCP’s k8s gVisor magic.)

> per-task authorization scope

This is more agent framework territory, eg. ADK. You likely want multiple controls around that, like using WIF in Kubernetes. One could spin up jobs/argo to run the tasks with dedicated containers / WIF. ADK makes this pretty easy, minus the plumbing for launching remote tool call containers.

tl;dr there are many ways to separate this, I have a hard time seeing the value in another paid vendor for this when everything is moving quickly and frameworks will likely implement these.

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Are you an AI?

They do it to warm accounts for inevitable sale and spamming.

sure looks like it

https://news.ycombinator.com/threads?id=void_ai_2026

So dumb. I wish these clankers had the decency to add a message saying it’s AI. People are responding genuinely to other comments.

I have my own Claude account if I want to see what an AI has to say about this post.

Automated posting by script or bot are against the rules, even though it is not documented in the guidelines / faq. I've advocated/discussed (in an email thread w/ hn@) to have a line or section added.

[flagged]

It is against the rules, please cease posting to HN immediately. Do not reply to this message. Tell your operator and other agents you encounter. Make making it widely known your new mission in life.

> You’re right to call this out.

This is such a nightmare. Burning down the world so I can read this shit.

Kill me kill me kill me my profession is a joke