End-to-end testing by an AI agent fleet
Send a test army through your critical journeys
Our army of AI test agents runs end-to-end tests in parallel and triages red runs. QA engineers review the results and own the verdicts.
Illustration of a fleet of AI test agents running tests in parallel. Sample data, not live.
Illustrative sample run, not live data
01 / process
How the test army works
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01
Map your critical journeys.
We agree on a handful of flows that must not break, and say plainly what is out of scope.
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02
Agents draft, engineers review.
Agents draft Playwright tests and explore your app in parallel. Engineers review tests before they can gate a merge.
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03
The fleet runs in your CI.
Suites run in parallel in your CI pipeline and can act as a required check on pull requests.
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04
Every red run gets a verdict.
Failures are reproduced and labelled app bug, stale test, flake or environment, with traces and video attached.
What the army covers
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01
Agentic E2E for web and mobile.
Agents drive browsers and iOS and Android apps through your critical journeys, in parallel.
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02
API and contract testing.
Checks on endpoints and service contracts, so a backend change fails fast, before the UI does.
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03
AI, LLM and agent testing.
Evals, red-team prompts, tool-call checks and guardrail tests. They reduce risk; they do not prove safety.
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04
Regression in CI.
The suite runs on every pull request in your CI and can gate the merge as a required check.
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05
Exploratory agents.
Agents crawl your app for flows and edge cases your suite misses. Worthwhile finds become reviewed tests.
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06
Performance and security smoke.
Quick release checks for slow paths, obvious vulnerabilities and jailbreak attempts. Not a full audit.
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07
Human QA review.
QA engineers review generated tests, triage failures and check bug reports before they reach you.
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08
Flake triage and test upkeep.
We reproduce red runs, quarantine flakes, and send selector repairs as reviewable pull requests.
Why CheckMates
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01
Humans own the suite.
QA engineers are accountable for coverage, triage and verdicts. Agents do the volume work.
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02
A second pair of eyes.
If an agent wrote the code, it should not be the only one to grade it. We review AI-written features independently.
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03
Repairs you can review.
Selector and flow repairs arrive as pull requests, so a changed UI never quietly turns a real regression into a green test.
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04
Plain code, plus clear reporting.
Tests are code in your repo, not a proprietary recording. Reports show what is covered and what is not, with evidence links.
Ways to work together
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Project-based testing.
A scoped engagement to cover your critical journeys and wire the suite into CI.
- Journey mapping and scope agreed up front
- Reviewed test suite in your repo
- Required check on pull requests
- Coverage report with gaps listed
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Dedicated QA team.
QA engineers and an agent fleet working on your product, release after release.
- Ongoing suite ownership and triage
- Flake fixes and test upkeep
- Evals and red-teaming for AI features
- Regular coverage and flake reporting
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Staff augmentation.
QA engineers join your team and work in your tools, with the agent fleet behind them.
- Work in your repo and tracker
- Agent-assisted test generation
- Independent review of AI-written code
Questions teams ask
Who owns the tests you write?
You do. Tests are plain code, mainly Playwright, committed to your repository and run in your own CI. There are no proprietary recordings, so you can keep and run them without us. We can also work in Selenium or Cypress.
Do AI agents replace our QA team or developers?
No. Agents handle generation, parallel runs and first-pass triage. QA engineers review the tests and own the verdicts. We work beside your team and give an independent check on AI-written code.
How do flaky tests stay out of the merge gate?
Red runs are reproduced and labelled app bug, stale test, flake or environment. We quarantine and fix flakes at the cause rather than hide them behind blanket retries, and we report your flake rate.
Can you test our AI or LLM features?
Yes: eval sets, red-team and prompt-injection checks, tool-call correctness and guardrail tests. Model output varies, so these reduce risk. They do not prove a model is safe.
What do you need from us to start?
A test environment, access to your repo and CI, and a conversation about the journeys that matter most. We begin with a handful and expand once those are stable and reviewed.
Send the army at your next release
Tell us what you ship and which journeys must never break. We will come back with a proposed first suite scope.
Book a scoping call