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.
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?
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/
I love the idea of emailing agents like we email humans! Thank you for sharing your learnings:
1. Network constraints vary quite a bit from one enterprise customer to another, so right now this is something we handle on a case-by-case basis with them.
2. We came to the same conclusion. For sensitive credentials like LLM API keys, we generate ephemeral keys so the real keys never touch the sandbox.
3. Totally right, we support constrained tasks too (ask mode, automated CI fixes). We've gone back and forth on whether to go vertical-first or stay generic. We're still figuring out where the sweet spot is. The constrained tasks are more reliable today, but the open-ended ones are where teams get the most leverage.
On the cost for solo devs, yeah, if you're one person running one agent at a time on your laptop, the sub is probably the better deal today. No argument there. The cloud agent model starts to make sense when you want to fire off multiple tasks in parallel.
Also you can fire and forget tasks (my favorite) and don't have to keep your laptop running at night.
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?
This assertion needs some support for those of us that don't have a macro insight into the industry. Are you seeing this from within FAANG shops? As a solo developer? What? Honest question.
Cloud starts to matter when you want to (a) run a swarm of agents on multiple independent tasks in parallel, (b) share agents across a team, or (c) not worry about keeping a machine online
Other than that, I agree with what you said. I don't know what the tradeoffs for local on-premises and cloud agents are in terms of other areas like convenience, but I do think that scalability in the cloud is a big advantage.
And so the SWE workflow is pre-built (research, planning, verification, PR, proof of work). Twill is also agnostic to the agent, so you can use codex for instance. Additionally you have more flexibility on sandbox sizing on Twill
Are there benchmarks out there that back this claim?