Hey HN, we're Willy and Dan, co-founders of Twill.ai (https://twill.ai/). Twill runs coding CLIs like Claude Code and Codex in isolated cloud sandboxes. You hand it work through Slack, GitHub, Linear, our web app or CLI, and it comes back with a PR, a review, a diagnosis, or a follow-up question. It loops you in when it needs your input, so you stay in control.

Demo: https://www.youtube.com/watch?v=oyfTMXVECbs

Before Twill, building with Claude Code locally, we kept hitting three walls

1. Parallelization: two tasks that both touch your Docker config or the same infra files are painful to run locally at once, and manual port rebinding and separate build contexts don't scale past a couple of tasks.

2. Persistence: close your laptop and the agent stops. We wanted to kick off a batch of tasks before bed and wake up to PRs.

3. Trust: giving an autonomous agent full access to your local filesystem and processes is a leap, and a sandbox per task felt safer to run unattended.

All three pointed to the same answer: move the agents to the cloud, give each task its own isolated environment.

So we built what we wanted. The first version was pure delegation: describe a task, get back a PR. Then multiplayer, so the whole team can talk to the same agent, each in their own thread. Then memory, so "use the existing logger in lib/log.ts, never console.log" becomes a standing instruction on every future task. Then automation: crons for recurring work, event triggers for things like broken CI.

This space is crowded. AI labs ship their own coding products (Claude Code, Codex), local IDEs wrap models in your editor, and a wave of startups build custom cloud agents on bespoke harnesses. We take the following path: reuse the lab-native CLIs in cloud sandboxes. Labs will keep pouring RL into their own harnesses, so they only get better over time. That way, no vendor lock-in, and you can pick a different CLI per task or combine them.

When you give Twill a task, it spins up a dedicated sandbox, clones your repo, installs dependencies, and invokes the CLI you chose. Each task gets its own filesystem, ports, and process isolation. Secrets are injected at runtime through environment variables. After a task finishes, Twill snapshots the sandbox filesystem so the next run on the same repo starts warm with dependencies already installed. We chose this architecture because every time the labs ship an improvement to their coding harness, Twill picks up the improvement automatically.

We’re also open-sourcing agentbox-sdk, https://github.com/TwillAI/agentbox-sdk, an SDK for running and interacting with agent CLIs across sandbox providers.

Here’s an example: a three-person team assigned Twill to a Linear backlog ticket about adding a CSV import feature to their Rails app. Twill cloned the repo, set up the dev environment, implemented the feature, ran the test suite, took screenshots and attached them to the PR. The PR needed one round of revision, which they requested through Github. For more complex tasks, Twill asks clarifying questions before writing code and records a browser session video (using Vercel's Webreel) as proof of work.

Free tier: 10 credits per month (1 credit = $1 of AI compute at cost, no markup), no credit card. Paid plans start at $50/month for 50 credits, with BYOK support on higher tiers. Free pro tier for open-source projects.

We’d love to hear how cloud coding agents fit into your workflow today, and if you try Twill, what worked, what broke, and what’s still missing.

I’ve been developing an open-source version of something similar[1] and used it quite extensively (well over 1k PRs)[2]. I’m definitely believer of the “prompt to PR model”. Very liberating to not have to think about managing the agent sessions. Seems that you have built a lot of useful tooling (e.g., session videos) around this core idea.

Couple of learnings to share that I hope could be of use:

1) Execution sandboxing is just the start. For any enterprise usage you want fairly tight network egress control as well to limit chances of accidental leaks or malicious exfiltration if theres any risk of untrusted material getting into model context. Speaking as a decision maker at a tech company we do actually review stuff like this when evaluating tools.

2) Once you have proper network sandboxing, you could secure credentials much better: give agent only dummy surrogates and swap them to real creds on the way out.

3) Sandboxed agents with automatic provisioning of workspace from git can be used for more than just development tasks. In fact, it might be easier to find initial traction with a more constrained and thus predictable tasks. E.g., “ask my codebase” or “debug CI failures”.

[1] https://airut.org [2] https://haulos.com/blog/building-agents-over-email/

The agent-agnostic approach is interesting, but I think the bigger architectural question is what happens when you move beyond code generation into domains where the agent's output has real-world consequences — booking a flight, executing a trade, dispensing medical advice.

For code, the worst case is a bad PR that gets caught in review. For domain-specific agents handling real transactions, you need a fundamentally different trust model. The LLM can't be making the decisions — it needs to be constrained to intent parsing while deterministic logic handles execution. Sandboxing the runtime (what you're doing) is necessary but not sufficient. You also need to sandbox the decision space.

Curious whether you've seen demand for non-SWE agent workloads, or if the "prompt to PR" pattern is where most of the traction is right now.

So instead of using my Claude Code subscription, I can pay the vastly higher API rates to you so you can run Claude Code for me?

Congrats on the launch, the agentbox-sdk looks interesting, but seeing as the first commit was 3 days ago - I feel a little wary to use it just yet!

One question, do you have plans for any other forms of sandboxing that are a little more "lightweight"?

Also how do you add more agent types, do you support just ACP?

24/7 running coding agents are pretty clearly the direction the industry is going now. I think we'll need either on-premises or cloud solutions, since obviously if you need an agent to run 24/7 then it can't live on your laptop.

Obviously cloud is better for making money, and some kind of VPC or local cloud solution is best for enterprise, but perhaps for individual devs, a self-hosted system on a home desktop computer running 24/7 (hybrid desktop / server) would be the best solution?

Does it support running Docker images inside the sandbox?

How does this compare to Claude Managed Agents?

> Run the same agent n times to increase success rate.

Are there benchmarks out there that back this claim?