Project Name
Reducing Mobile QA Cycle Time 60% With Agentic Testing for a Product Studio
A digital product studio building and maintaining mobile and web applications for multiple clients simultaneously, spanning iOS, Android, React Native, and Next.js codebases, had every release running into the same bottleneck: a manual regression sweep checking that a feature behaved consistently across every platform before it shipped. QA engineers weren’t the bottleneck because the checks were hard; they were the bottleneck because there were too many of them to run by hand every sprint, and the studio wanted to accelerate releases without lowering its quality bar.
Ksolves brought AI ML consulting expertise to the problem, deploying a set of agentic QA and code-review agents scoped to specific, checkable tasks and integrated directly into the studio’s existing CI/CD pipeline. Mobile QA cycle time dropped 60%, and bug triage time fell by 55%.
- Cross-Platform Regression Checks Were Entirely Manual: QA engineers manually verified that the same feature behaved consistently across iOS, Android, and web builds before every single release.
- QA Capacity Didn't Scale With Concurrent Client Projects: Running the studio's model of parallel client engagements meant QA effort scaled linearly with project count, with nothing offsetting the load as it grew.
- Bug Triage Consumed Senior Developer Time: Senior developers spent significant time each sprint triaging and reproducing reported bugs before anyone could even assign them for a fix.
- Test Coverage Gaps Went Unnoticed Until Late in the Cycle: Coverage gaps in newer feature areas often only surfaced during manual regression, late enough to delay a release.
- Code Review Turnaround Varied Widely by Reviewer Availability: Pull requests sometimes sat for a day or more waiting on a senior developer's availability to review them.
Ksolves brought AI ML consulting expertise to the problem, scoping each agent to specific, checkable tasks rather than open-ended "AI-assisted development," with every finding surfaced for a human developer to confirm before anything merges.
- Cross-Platform Consistency Agent: Compares iOS, Android, and web implementations of the same feature against a shared spec, flagging behavioral drift automatically.
- Automated Regression Test Generation: Generates and maintains regression test cases for newly added features, closing coverage gaps as code ships rather than discovering them after the fact.
- Bug Triage and Reproduction Agent: Pre-triages incoming bug reports, attempting reproduction and attaching relevant logs and stack traces before routing to a developer.
- First-Pass Code Review Agent: Runs an automated first-pass review on every pull request, flagging style, security, and common-bug patterns before a human reviewer's pass even starts.
- CI/CD Pipeline Integration: Every agent wires directly into the existing build pipeline so findings surface automatically without changing the team's git workflow at all.
Technology Stack
| Category | Technology |
|---|---|
| AI/ML | Agentic Coding and QA Framework |
| DevSecOps | CI/CD Pipeline Integration |
| AI/ML | Large Language Model (Coding-Focused) |
| Integration | Version Control and Issue Tracker APIs |
| Infrastructure | Build and Test Infrastructure |
- Mobile QA Cycle Time Cut by 60%: Agent-assisted regression QA now averages 1.2 days per release, down from a full cross-platform regression cycle that used to average 3 days.
- Bug Triage Time Cut by 55%: Pre-triaged reports with reproduction already attached now take under 2 hours per sprint, down from roughly 4 hours senior developers used to spend triaging and reproducing bugs by hand.
- Code Review Turnaround Improved by 45%: First-pass agent review cut average pull request turnaround to 14 hours, down from 26 hours.
- Test Coverage Gaps Caught Before Release Up to 92%: Automated regression generation now catches an estimated 92% of coverage gaps before release, up from roughly 70% under the manual process.
“Our QA team isn’t smaller, but our release cycle got a lot faster, because the agents catch the repetitive cross-platform stuff before it ever reaches a person. Our senior developers got a meaningful chunk of their week back.”
– VP of Engineering, Technology
Manual QA and bug triage across iOS, Android, and web codebases constrained this product studio’s release velocity, and senior developers kept losing sprint time to reproduction work the checks themselves didn’t actually need. Ksolves brought AI ML consulting expertise to deploy scoped agentic QA and review agents that run automatically on every build and pull request.
Mobile QA cycle time dropped 60%, bug triage time fell 55%, and code review turnaround improved 45%, directly increasing sprint throughput across the studio’s concurrent client projects. Coverage gaps that used to slip through until late in the cycle now get caught before release almost every time.
The studio is now extending the same agent pattern to automated release-note generation as the next step.
Curious How Agentic QA Could Speed Up Your Release Cycle?
