Service

Agentic AI Quality Assurance for software teams

Autonomous AI agents that plan tests, run regression, and heal flaky assertions — so your release confidence keeps up with your roadmap. UK engineering team, global clients.

Promise
  • Autonomous coverage — agents explore the product and propose what to test next
  • Self-healing assertions that survive UI and copy changes
  • CI-native — runs on every PR with evidence, not opinions
  • UK-based delivery for fintech, healthtech, and regulated platforms

What you get

Agentic test plan

AI agents map your product's critical journeys and propose a prioritised regression pack — reviewed by a senior QA lead before anything ships.

Autonomous regression suite

Agents run the suite, decide what to retry, and quarantine genuinely broken paths — your engineers see signal, not flake noise.

Self-healing assertions

When selectors or copy drift, the agent adapts the locator and flags the change for review. Locator-rot stops being a maintenance tax.

CI integration

PR gates, scheduled deep-runs, and traceable evidence — wired into GitHub Actions, GitLab CI, Jenkins, or your in-house runner.

Quality dashboards

Pass rate, flake rate, coverage by critical journey, and time-to-green per release — the numbers leadership actually asks for.

Dedicated QA lead

A named senior engineer owns your suite end-to-end. No ticket queues, no offshore handoffs, no surprise rotations.

How it works

  • Exploratory agents crawl the app, log every reachable state, and surface the critical paths that block revenue or compliance
  • We pair agent output with a senior QA lead to prioritise — risk-weighted, not 'test everything'

Evidence you will actually see

Run links per PR + per release with logs, screenshots, and traces
Self-healing change-log — every locator the agent adapted, with diff and reviewer
Trend snapshots: pass rate, flake rate, runtime, and coverage by critical journey
Failure clustering — top failing tests and the components driving regression

Releases you can trust, without expanding the QA team.

Tools & stack

Playwright + agentic layer

Playwright as the deterministic engine; our agent layer drives exploration, self-healing, and intent-based assertions.

Appium / Detox / Maestro

Same agentic layer applied to mobile — see our autonomous mobile app testing service for the full stack.

GitHub Actions / GitLab CI / Jenkins

PR gates, scheduled regression, and parallelised runners — wired into your existing pipeline, not a parallel one.

OpenAI / Anthropic / open-weights

Model-agnostic agent runtime — we choose the right model per task and per data-sensitivity boundary.

Jira / Linear / TestRail

Traceability from critical journey → test → run → ticket. No copy-pasting screenshots into Slack.

Grafana / Looker Studio

Quality dashboards mounted where your engineering leadership already looks.

FAQs

What is agentic AI quality assurance, and how is it different from traditional test automation?+
Traditional automation runs the exact scripts you wrote. Agentic AI QA adds autonomous agents on top of that engine — they explore the product, decide what to test next, and adapt assertions when the UI drifts. You still get deterministic Playwright/Appium runs underneath; the agentic layer is what removes the maintenance tax.
Are you a UK-based agentic AI QA testing company?+
Yes. We're based in the UK, deliver in UK working hours, and have hands-on experience with UK fintech, healthtech, and regulated platforms. We work with global clients but the senior engineering lead on every engagement is UK-based.
How quickly can we see results?+
First agentic regression pack typically runs in CI within 2–3 weeks: discovery in week one, stable baseline in week two, full PR-gate integration by week three. After that the suite grows weekly as agents surface new critical paths.
Do you only test web apps, or also mobile?+
Both. For mobile-specific engagements, see /services/autonomous-mobile-app-testing/. For web-focused work, see /services/ai-web-application-testing/. Many clients run both under one agentic-AI QA programme.
How do you handle data privacy when AI agents touch our product?+
We isolate the agent runtime to your staging environment by default, run model calls through self-hosted or enterprise endpoints when required (UK fintech and healthtech clients usually do), and never feed production PII to a public model. The policy is set on day one and audited per engagement.
What happens to my existing Playwright or Selenium suite?+
We keep it. The agentic layer wraps your existing tests rather than replacing them — your team keeps ownership of the code, and the agents handle the maintenance work that used to drain engineering time.
Can the agents work with React, .NET, Java, or other complex backends?+
Yes. The agentic layer is product-shape-agnostic — it operates against the UI and API surface. We've worked across React/Next.js, Angular, .NET, Java, and Rails backends with no special accommodation.
What does pricing look like?+
We charge per dedicated senior QA lead plus the agent runtime, not per test case. A typical engagement is one named lead embedded with your team, scaled up with extra leads as coverage grows. Talk to us for a concrete quote based on your stack and release cadence.
Proof · Case studyAgentic QA wired in from commit oneRead the case study
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