Your AI agents write code all day. QA Agents spin up for every pull request, test it against your real app, and answer GO or NO-GO, with evidence, before anything reaches your users.
Nobody checks the product: the limits, the permissions, the flows your users actually take.
Tests only cover what someone wrote.
New behaviour from an agent rarely comes with new tests, and regressions slip through.
QA environments sit idle.
Always-on staging and manual QA don't scale to a hundred PRs a day.
QA Agents give every change a tester who knows your product, runs your real app, and leaves proof.
How it works
From pull request to verdict, on the fly.
A QA Agent exists only for the life of a request. It appears when a PR lands, tests, decides, and disappears.
⑂
PR opened
Your coding agent pushes a change
◳
QA Agent spins up
A fresh sandbox from your environment snapshot, in ~2 s
§
Plans from your rules
New cases for the change, regression from the knowledge base
✓
You approve
Or a QA-manager agent reviews the plan
▶
Tests the real app
UI, APIs and data, with screenshots and responses
⟲
Verifies
Re-checks evidence, re-runs failures to rule out flakes
●
GO / NO-GO
A verdict, and defects with reproduction steps
✕
Gone
The sandbox is deleted. Nothing idles, nothing to pay
On demand
Pay per request. Never for idle machines.
A QA environment that waits around all day costs money all day. QA Agents only exist while a PR is being tested.
Always-on QA environment
~$120 / month per machine, busy or not
QA Agents
Billed only during each request
00:0006:0012:0018:0024:00
Estimate your month
$32per month, about $0.16 per request
Claude usage $30billed to your own key
Sandbox compute $2.00~4 min per request
vs. one always-on QA machine$120
Based on measured runs: an 8-case test request took about 1 minute of model time and $0.12–0.20 of Claude usage. Bigger changes cost more. Signals itself is free during the preview.
The product
See exactly what your QA Agents did.
A clear GO or NO-GO on every change
Five visible stages from plan to verdict
Cost, time and turns of every run
Every case tied to the business rule it protects
signals.qacart.com
evidence/tc-08-ui-limit.png
Evidence, not opinions
Every claim comes with a screenshot.
This is what a QA Agent actually saw while checking that a Free user is stopped at 3 todos. A pass needs proof too.
✓TC-08UI shows limit state at 3 todos on FreeR7
✕BUG-014th todo accepted on the Free planNO-GO
Security & control
We provide QA Agents, not AI.
Your code and prompts go through your own Claude account, under your data terms. We never resell or mark up model usage.
Your key, encrypted. Stored in an encrypted vault and handed only to the sandbox running your request.
One sandbox per request. Fresh every time, deleted afterwards, in the EU.
Mocks, not real services. Payments, email and OAuth are never called for real during tests.
Your cloud, your contract. Use Claude through AWS Bedrock, Google Vertex AI or Microsoft Foundry.
Your network, on request. Runners inside your own infrastructure are on the enterprise roadmap.
FAQ
Questions, answered.
Claude: Opus for planning and review, Sonnet for exploring and executing, Haiku for simple checks. You bring your own key from Anthropic, or use Claude through your AWS Bedrock, Google Vertex AI or Microsoft Foundry account.
Private preview
Put QA Agents on every pull request.
We're onboarding a small number of teams that build with AI coding agents. Tell us about yours and we'll send an invite.
Free during the preview
Guided onboarding of your first project
You only pay Anthropic for model usage, per request