How it works

You finish the change. That’s the whole job.

No release plans to write, no rollout configs, no babysitting. Fireweave picks up every change the moment it’s ready and walks it into production.

drive a rollout yourself →
  1. Install once. Run the Fireweave CLI install and fw init so ship-safe skills are injected into your coding agents.
  2. Build in your agent. Ship the feature the way you already work — any coding agent. Changes come out born rollout-ready at a control point.
  3. Fireweave ramps cohorts. Fireweave drafts a change-specific release plan and advances only when each cohort proves stability and adoption.
  4. Guardrails and rollback. If signals dip, Fireweave pauses or auto-reverts via a control-point flip — without a manual war room.
00 One-time setup your workflow doesn’t change
terminal
$ curl -fsSL get.fireweave.dev/install | sh
✓ fireweave CLI installed · signature verified
$ fw init
✓ skill installed into your coding agents
✓ ship-safe instructions injected
✓ adoption telemetry wired
Claude Coderollout-ready
Cursorrollout-ready
any agent…rollout-ready

Run it once. From then on your agents keep working exactly as they do today — same prompts, same flow — and every change they build comes out born rollout-ready: wrapped at a control point, safe to ship, adoption measured from its first user.

Safe by design — signed binaries · least-privilege tokens · your code and secrets never leave your infra · every injected line is reviewable code in your repo.
01

You build

in your coding agent

Ship the feature the way you already work — any coding agent, nothing new to learn.

⎓ feat/your-feature· any agent
02

Fireweave ships it

cohort by cohort

It drafts the release plan for your change and advances only when each cohort proves it out.

03

You watch it land

stability · adoption

Two live signals tell you it’s healthy and being used — and it reverts itself the instant one dips.

stabilityadoption

No new tool to learn. No YAML to write. The rollout starts where the code starts.

The learning engine

The loop doesn’t repeat. It accelerates.

Every rollout Fireweave runs becomes evidence for the next one — gates tighten, cohorts sharpen, ramps shorten. Watch one season of releases: regressions get caught at 1% instead of 60%, and rollbacks fire before users notice.

SHIPOBSERVELEARNMATCHDECIDERELEASE 1LAP 6.0s0 patterns banked
Median catch point
60% exposure60%
User impact per regression
41 min<41 min
Time to 100%
6d 2h146h

an illustrative season · your numbers are the product