AI Root Cause Analysis Agent — Real Defect or Flaky Test?
Pcloudy's AI Root Cause Analysis Agent — part of the QPilot.AI platform — inspects every failed session and build on 5,000+ real devices, clusters error patterns, separates real bugs from flakiness, and tells your team exactly what broke and why.
Test failures pile up. Triage doesn't scale.
QA leads waste hours every release deciding what's a real bug, what's flaky, and what's environment noise. AI does it in seconds.
- QA leads scroll through thousands of log lines per failed run
- Same root cause re-investigated every release
- Flaky tests get re-run blindly until they pass
- Device-specific bugs slip through to production
- No way to tell defect from environment noise at scale
- Errors auto-clustered & ranked by frequency and impact
- Real defects flagged separately from flaky behaviour
- Build-level patterns aggregated across every session
- Device-specific failures called out by model & OS
- One-click triage: open the failing session, see the why
Anatomy of an analyzed failure
Every failed session comes back with the device, the error, the evidence and the AI verdict — so triage takes seconds, not hours.
NoSuchElementException:
id=checkout_btn not found
at CheckoutPage.tap_checkout (line 42)
- Screenshot at moment of failure
- Full device & Appium logs
- Network HAR + step timeline
Real defect — UI locator drift. The checkout_btn id was renamed to btn_checkout in build #4820. 15 of 18 failures across this build share the same locator.
- Update locator to btn_checkout
- Re-run checkout-suite on Pixel 4 XL
- Auto-file Jira ticket with this evidence
How the AI knows: defect vs flake
Not a coin toss. The agent weighs concrete signals across sessions, devices and builds before assigning a verdict.
- Reproduces on ≥2 devices in the same build
- Same error signature appears across consecutive builds
- Failure tied to a specific code change or release
- Deterministic — fails at the same step every run
- No environmental noise (network, device state) in logs
- One-off failure that doesn't repeat on retry
- Other devices in the same build pass cleanly
- Network jitter, ANR or device reboot in logs
- Timing-sensitive step (animation, async wait)
- Random step in the suite — no consistent failure point
AI bug triage, on every test run
Stop sifting through logs. The Failure Analysis Agent reads them for you.
Defect vs flake detection
AI separates real product defects from environment flakiness so your team triages the right bugs first.
Error trends & clustering
Similar errors are grouped across sessions, ranked by frequency and impact — biggest issues surface first.
Device & OS breakdown
Per-device pass/fail with compatibility hints. Spot Android-version or resolution regressions instantly.
Recurring pattern alerts
When the same root cause shows up across builds, you're alerted before it cascades into a release blocker.
Performance flags, surfaced automatically
Beyond pass/fail — the agent monitors execution speed, response times and UI responsiveness across every analyzed session.
Pages taking longer than expected to render. Catch front-end regressions before users do.
UI elements behaving inconsistently across runs. Surface intermittent rendering bugs.
Navigation steps that can be optimised away. Tighten test flows automatically.
API calls exceeding response thresholds. Spot backend bottlenecks across builds.
Lands in the tools you already use
Trigger from CI, analyze every failure, deliver verdicts where your team already triages — no extra dashboards to learn.
Your existing test suite
Drop in your suite — no rewrites, no SDK installs.
Straight from your CI
Kick off runs from any pipeline you already operate.
Verdicts where you triage
Real defects vs flaky tests — pushed to your team.
More than a test report
Standard test reports tell you what failed. The Failure Analysis Agent tells you why, where to look, and what to do next.
Built for Regulated Industries
Test artifacts are scoped to your tenant, encrypted in transit and at rest, and never used to train foundation models — meeting PCI-DSS, SOC 2 Type II, and ISO 27001 requirements. PII redaction patterns can be configured for sensitive fields including banking credentials, OTP values, and card numbers. Access is governed by role-based permissions and SSO.
How root cause analysis works
Run your tests
Use your existing Appium / Selenium / Espresso / XCUITest suite on Pcloudy real devices — no changes.
Enable AI Analysis
Toggle Session-Level AI Analysis on any failed session. Build-Level Insights aggregate automatically.
Get clustered insights
Errors are grouped, performance flags surfaced, and device-specific failures highlighted.
Triage with confidence
Real defect, flaky test, or environment issue — the agent tells you which, and where to look.
Real triage scenarios, solved automatically
Top error cluster: ELEMENT NOT FOUND
Scenario: 15 sessions across the build hit the same locator failure on the checkout button.
AI Insight: AI flags it as a real defect — UI locator drift after the latest release. Fix once, suite goes green.
Recurring slow_api_responses
Scenario: 8 sessions trip the API response threshold on /v2/cart over the last 3 builds.
AI Insight: Backend regression surfaced before users complain. Investigate endpoint or raise threshold intentionally.
Device-specific failure pattern
Scenario: Pixel 4 XL fails 3 of 3 sessions while every other device passes.
AI Insight: Android 11 / screen-resolution compatibility issue isolated. Targeted fix instead of suite-wide investigation.
Built for the whole quality team
QA Leads
Stop drowning in failure logs. Get a ranked, clustered view of every release's real issues in one place.
SDETs
Skip the log-grepping. Jump straight to the failing session, see device, OS, locator and stack — all triaged.
Engineering Managers
Build-level dashboards make release health visible. Track flake rate, defect rate, and device coverage over time.
Release Owners
Ship with confidence. Know exactly which failures are blockers and which are noise before sign-off.
Questions, answered
What is an AI root cause analysis agent for test automation?
An AI root cause analysis agent automatically inspects failed test sessions and builds, correlates signals across logs, screenshots, network traces and device telemetry, and tells your team why a test failed — not just that it failed. Pcloudy's agent clusters similar errors, separates real defects from flaky tests, and surfaces device- or OS-specific patterns across runs on 5,000+ real Android and iOS devices.
How does the AI tell defects from flaky tests?
It correlates failure patterns across sessions, devices and builds. Failures that reproduce on multiple devices, repeat across consecutive builds, and fail at the same step are flagged as real defects. One-off failures that don't repeat on retry, with environmental signals like network jitter, ANRs or timing-sensitive steps, are flagged as flakiness — so your team triages real bugs first.
How does root cause analysis reduce test maintenance time?
Instead of QA leads scrolling through thousands of log lines per failed run, errors are auto-clustered and ranked by frequency and impact. The same root cause isn't re-investigated every release, suggested actions are pre-filled per cluster, and verdicts can be pushed straight into Jira or Slack — collapsing hours of triage into minutes.
Is the Root Cause Analysis Agent suitable for banking and fintech QA?
Yes. Test artifacts are scoped to your tenant, encrypted in transit and at rest, and never used to train foundation models — meeting PCI-DSS, SOC 2 Type II and ISO 27001 requirements. PII redaction patterns can be configured for sensitive fields like banking credentials, OTP values and card numbers, and access is governed by role-based permissions and SSO.
How does the agent detect device-specific and OS-specific failures?
Every failure is analysed against a per-device, per-OS pass/fail matrix. When a test fails only on a specific device model, screen resolution or Android/iOS version while passing elsewhere, the agent isolates it as a compatibility issue — so you fix one model instead of investigating the whole suite.
Can it predict failures before they happen?
Recurring patterns across builds are surfaced as alerts so the same root cause doesn't quietly cascade into a release blocker. Performance flags like slow_page_load, flaky_ui_elements and slow_api_responses are tracked over time, giving teams an early signal on regressions before users see them.
How does AI root cause analysis differ from standard test reports?
Standard reports give you a flat pass/fail list. The AI Root Cause Analysis Agent groups similar errors, ranks them by impact, assigns a defect-vs-flake verdict, highlights device- and OS-specific failures, and recommends a next action per cluster — turning a report into a triage workflow.
How are logs and screenshots handled for regulated industries?
Logs, screenshots and videos stay scoped to your tenant, encrypted in transit and at rest, and are never used to train foundation models. The platform is built to meet PCI-DSS, SOC 2 Type II and ISO 27001 requirements, with configurable PII redaction for sensitive fields and access controlled by RBAC and SSO — suitable for banking, fintech, healthcare and other regulated workloads.
Does it work for web sessions or only mobile?
Both. The agent analyses real-device mobile sessions on Android and iOS as well as real-browser web sessions on Pcloudy — so cross-platform teams get the same clustering, verdicts and pattern alerts across every surface.
How does the Root Cause Analysis Agent integrate with CI/CD and Jira?
Trigger Pcloudy runs from Jenkins, GitHub Actions, GitLab CI, Azure Pipelines, CircleCI or Bitbucket Pipelines. Verdicts come back as build status, artifacts and webhooks, and real-defect clusters can be pushed into Jira, Linear or Azure Boards with the failing session, device, error and AI verdict pre-filled — so triage ends in a ticket, not a meeting.
