TryCase

Disposable test environments for AI coding agents

Software Engineering

Developer Tools

Artificial Intelligence

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As a solo dev shipping an iOS app, the final manual-verify step is exactly where my releases stall — I'm the QA team of one, and "done" from an agent usually just means "it compiled." The disposable box per agent is a smart way to move that check off my laptop. Does it handle mobile/app flows (simulator or a device) yet, or is it web + CLI apps only for now?

This maps to the messy part of agent coding for me: not the patch itself, but proving it ran in a clean environment. The useful constraint is keeping the proof lightweight enough that agents actually include it every time.

Love how the screenshots and recordings come back with the diff already attached, makes verifying what the agent actually did way less of a guessing game.

the disposable linux envs actually working from a single prompt blew me away, screenshots and all. finally lets the agent go end to end without me babysitting every shell command.

how does it handle cleanup of those disposable environments when an agent spins up a bunch in parallel, anything to worry about resource-wise?

finally something that lets my agent actually run the code instead of me playing QA. tried it on a small flask app and got back screenshots plus a clean diff, which honestly saved me a whole round trip.

Congrats on the launch, this is a real problem. My question is more about the iteration loop than the verification side - if an agent fails, tweaks the code, and needs to test again, does it get a brand new disposable box each time, or does it reuse the same one until the task is done? Fully fresh every retry sounds cleanest for isolation but on a repo with a slow install/build step that could add up fast if the agent is iterating 10+ times on one bug.

I’m curious about the differences between GPT-5.5 browsing and testing and TryCase. What makes TryCase unique, apart from just screenshots and videos? Thank you

How do you handle state between runs if the agent needs to verify something like a running database or queued background jobs from a previous step?

Such a smart concept for vibe coding. Testing AI-generated code safely without messing up my local environment is always a massive concern. Can these disposable environments be spun up locally, or is the platform entirely cloud-based?

Screenshots and recordings as verification artifacts are useful for UI changes, but for backend logic or API behavior the visual output doesn't tell you much about whether the code actually works correctly. What does TryCase return as verification evidence for non-visual changes, like a database write, a webhook handler, or a background job, and how does the agent know the difference between "it ran without error" and "it did the right thing"?

the disposable Linux environment idea is such a clean solve for the "I cant actually verify this" problem with coding agents. nice execution.

finally something that lets my coding agent actually run the app instead of just staring at it. the screenshot capture came back clean on the first try

The 'an agent handles verification' step is where I've watched this quietly break. When the same model family does the work and the check, the verifier tends to trust the doer's framing of what success looks like, so it happily confirms a screenshot of the wrong screen. What helped me was feeding the verification agent only the original task spec plus the artifact, never the doer's transcript, so it can't inherit the optimistic story of what happened. Does TryCase hand the checker the full run log, or just the recording and a fresh prompt?

'Return verified code instead of asking you to test manually' is the exact gap. My coding agent writes the fix, then I'm the one clicking through the app like it's 2015. The agent proving its own work with screenshots and recordings flips the trust equation completely. How isolated are the environments - can it safely test against a copy of production data? Congrats on the launch.

congrats on the launch ben. the port collision thing you describe hit me the first time i let two agents run dev servers at once, so the throwaway box approach makes a lot of sense. one thing i'm curious about: booting an app end to end usually means real env vars, api keys, db urls. where do those secrets live while a sandbox runs, and does teardown wipe them for good?

the 'done only after it actually ran and verified' bar is the right one. do you surface the failed attempts too, or just the final passing proof?

"agents should only say done once they've actually run and verified the work" is exactly right and it's the thing i keep running into with my own multi-agent setups - an agent will confidently report success based on its own transcript when the thing it was supposed to do never actually happened underneath. running multiple worktrees locally does turn into port collisions and reused browser sessions pretty fast like you said. does the screenshot/recording proof get attached anywhere the agent itself can't fake or reword, or is it still on me to actually look at the video rather than trust the agent's summary of it?

Love the framing around agents only saying done after screenshots/video proof in an isolated env — that is exactly the gap when running multiple worktrees locally. Good luck with the launch.