AI agents that test mobile apps like human QA

Qualgent runs AI agents on real iOS and Android devices. Tests are written in plain English; self-learning agents adapt to UI changes and run in parallel across device farms.

PythonReactNode.jsComputer VisionLLMsWebSockets

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60×
Faster regression testing
90%
Less test maintenance
#3
App Store rank reached by a customer
5
Core systems shipped

The client

AI agents for mobile QA

Qualgent.ai uses AI agents to mimic real human testers on actual devices. No scripts, no selectors, no maintenance overhead — and parallel execution across device farms turns a three-day regression into minutes.

The problem

QA became the bottleneck of AI-accelerated development

As AI makes coding faster, teams ship code faster than they can test it. Manual mobile QA does not scale.

Script-based tools like Appium are flaky and break on every UI change, creating a maintenance loop that defeats the purpose of automation.

Manual QA cannot keep pace with AI-accelerated development
Appium / XCTest scripts break on every UI change
Maintenance cost exceeded the value of automation
No parallel execution across real devices
3-day regression cycles blocking weekly releases

What we built

5 systems, one platform

Violet marks the parts where the AI does the work. A human approves every consequential change.

Module 01
Cloud device management
Provisioning, health monitoring, session management and auto-recovery for real iOS and Android devices in the cloud.
- Real device provisioning
- Parallel execution across farms
- Health monitoring & recovery
- Session & resource allocation
Module 02
Web dashboard & test management
Author tests in plain English, organise suites, schedule runs and review results with video and step logs.
- Plain-English authoring
- Suites & scheduling
- Video playback per run
- Step-by-step logs
Module 03
Integration layer
GitHub, Slack and Linear integrations plus developer APIs so tests run on every build and post results to PRs.
- GitHub, Slack, Linear
- CI/CD developer APIs
- Webhooks
- Trigger on push
Module 04 AI does this
AI agent orchestration
Computer-use LLM agents and vision models tap, swipe, type and speak like a human tester — and self-heal when the UI changes.
- Vision-driven device interaction
- Self-healing tests
- Natural-language interpretation
- Multi-step context
Module 05
Reporting pipeline
Video, step logs and pass/fail evidence for every run, with failure categorisation and trends.
- Full video per run
- Pass/fail with evidence
- Failure categorisation
- Exportable reports

Stack

PythonReactNode.jsComputer VisionLLMsWebSocketsDockerCloud infrastructure

Dedicated Teams·SaaS & Technology

Outcome

60× faster regression testing.

More proof

Other systems we've shipped

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Faster compliance cycles

Read the case