Here's a demo: https://www.youtube.com/watch?v=ttMl96l9xPA.
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.
We wanted something like Cog from Replicate, but for agents: a simple way to package agent code from a repo and serve it behind a clean API/SDK. We wanted to provide a protocol to communicate with your agent, but not constraint the agent logic or harness itself.
On Terminal Use, you package your agent from a repo with a config.yaml and Dockerfile, then deploy it with our CLI. You define the logic of three endpoints (on_create, on_event, and on_cancel) which track the lifecycle of a task (conversation). The config.yaml contains details about resources, build context, etc.
Out of the box, we support Claude Agent SDK and Codex SDK agents. By support, we mean that we have an adapter that converts from the SDK message types to ours. If you'd like to use your own custom harness, you can convert and send messages with our types (Vercel AI SDK v6 compatible). For the frontend, we have a Vercel AI SDK provider that lets you use your agent with Vercel's AI SDK, and have a messages module so that you don't have to manage streaming and persistence yourself.
The part we think is most different is storage.
We treat filesystems as first-class primitives, separate from the lifecycle of a task. That means you can persist a workspace across turns, share it between different agents, or upload / download files independent of the sandbox being active. Further, our filesystem SDK provides presigned urls which makes it easy for your users to directly upload and download files which means that you don't need to proxy file transfer through your backend.
Since your agent logic and filesystem storage are decoupled, this makes it easy to iterate on your agents without worrying about the files in the sandbox: if you ship a bug, you can deploy and auto-migrate all your tasks to the new deployment. If you make a breaking change, you can specify that existing tasks stay on the existing version, and only new tasks use the new version.
We're also adding support for multi-filesystem mounts with configurable mount paths and read/write modes, so storage stays durable and reusable while mount layout stays task-specific.
On the deployment side, we've been influenced by modern developer platforms: simple CLI deployments, preview/production environments, git-based environment targeting, logs, and rollback. All the configuration you need to build, deploy & manage resources for your agent is stored in the config.yaml file which makes it easy to build & deploy your agent in CI/CD pipelines.
Finally, we've explicitly designed our platform for your CLI coding agents to help you build, test, & iterate with your agents. With our CLI, your coding agents can send messages to your deployed agents, and download filesystem contents to help you understand your agent's output. A common way we test our agents is that we make markdown files with user scenarios we'd like to test, and then ask Claude Code to impersonate our users and chat with our deployed agent.
What we do not have yet: full parity with general-purpose sandbox providers. For example, preview URLs and lower-level sandbox.exec(...) style APIs are still on the roadmap.
We're excited to hear any thoughts, insights, questions, and concerns in the comments below!
verdverm•2h ago
eg. I already run Kubernetes
jwoq9118•2h ago
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.
verdverm•2h ago
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)
instalabsai•2h ago
verdverm•2h ago
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.
goosejuice•2h ago
alexchantavy•1h ago
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.
verdverm•1h ago
tl;dr, I don't think the shovel analogy holds up for most of the Ai submissions and products we see here.
webpolis•33m ago
Most sandboxing approaches — including this one — optimize primarily for isolation from the host: prevent the agent from escaping, limit what it can touch. That solves the runaway agent problem.
But there's a second axis: observable execution for human collaborators. When an agent modifies a codebase or runs a research task, a teammate often needs to watch it happen in real time, intervene before it commits a wrong turn, or audit what actually ran. Async logs and artifact outputs don't cover this well.
We've been building Cyqle - https://cyqle.in -- (disclosure: I work on it) from that angle — cloud desktop sessions where agent runs are shared live with whoever needs visibility. Isolation is at the VM/session level rather than syscall granularity. Different tradeoff: you give up process-level permission mapping, you gain real-time collaborative access to the running environment — watch, intervene, hand off.
The use cases probably don't overlap much with Terminal Use (async batch filesystem agents clearly want deep process isolation + lifecycle APIs). But this thread made me think "agent environments" is actually several distinct problem spaces: async autonomous execution, interactive human-supervised sessions, team-observable debugging runs. The right primitives look very different depending on which you're solving.
debarshri•1h ago
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
verdverm•1h ago
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?
hrmtst93837•22m ago
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.