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The problem
AI made your engineers faster. It made your QA bottleneck worse.
DORA 2026 data shows the gap is widening — and it won't close with more headcount.
441%
More PR review time
AI-generated code creates more pull requests, more edge cases, more surface area — but the same number of QA engineers to review it all.
66%
More epics per developer
Engineers ship faster with Copilot and Cursor. QA velocity hasn't moved. Testing becomes the release gate, every sprint.
Manual maintenance kills ROI
A UI change breaks 40% of your test suite. Engineers spend days fixing selectors instead of shipping features. Automation projects die slowly.
Coverage stops at functional
Performance, visual regression, security, chaos — all gaps. Not because your team doesn't know. Because there's no capacity to cover them.
The solution
8 agents. One closed loop. Zero manual QA code.
Each agent takes the output of the previous one. The pipeline runs continuously — humans set direction, agents do the work.
1. Test сase сreation
Reads Jira tickets, Confluence specs, and design mockups. Generates structured test scenarios in minutes — coverage from requirements ≥90%, ticket-to-test in under 30 minutes.
2. Validation & Normalization
An LLM-as-judge reviews every test case for quality, coverage, edge cases, and naming consistency. Nothing proceeds until it passes. LLM-judge pass rate ≥95%.
3. Code generation
Produces production-ready automation scripts for API, Web, iOS, and Android — built against your existing framework and codebase. Human-reviewed before merge. First-pass compile rate ≥95%.
4. Test execution
Runs tests in parallel across 100 to 1,000 threads. Screenshots, logs, and pass/fail verdicts captured automatically. Average cycle time −50% vs baseline. Flakiness rate <2%.
5. Self-healing
When a UI change breaks a test, this agent detects the failure, fixes the selector, re-runs, and validates — in under 3 minutes. No human touch required. Auto-heal success rate ≥80%.
6. Failure analysis
Correlates failed tests with logs, screenshots, and historical patterns to produce instant root cause analysis. Feeds learnings back into Code Generation — the pipeline improves every cycle.
7. Notification
Delivers real-time Slack alerts and email summaries to QA leads and PMs the moment a run completes. Human confirms before any escalation — control stays with your team.
8. Report generation
Produces dashboards, quality KPIs, and trend visualizations — all mapped to DORA metrics and ready for leadership review. Report delivery <1 hour after run completion.
Business outcomes
Measured in the metrics your board already tracks.
Every agent maps to a DORA metric. Quality engineering becomes a business lever, not a cost center.
Deployment frequency
Automated regression gives teams the confidence to ship on every merge, not batch releases once a month out of fear.
Lead time for сhanges
Test cases created at story time. No manual sign-off queue holding up the pipeline between dev and deploy.
Change failure rate
NFR tests catch performance regressions, security gaps, and data issues before they reach production.
Recovery time (MTTR) frequency
Automated regression gives teams the confidence to ship on every merge, not batch releases once a month out of fear.
Unit economics
The math that makes continuous testing viable
$10
one-time cost to author a test
one-time cost to author a test
$0.50
per execution cycle, forever
run + self-heal + analyze
~12×
cheaper than manual QA regression
at enterprise labor rates
Break-even: ~20 runs
Each test pays for its $10 creation cost after roughly 20 execution cycles. With CI running on every merge, that's about three weeks
1,000 tests · 1 year
Prepped a Salesforce shop for Black Friday chaos with 50+ performance scenarios and zero crashe
The problem
AI made your engineers faster. It made your QA bottleneck worse.
DORA 2026 data shows the gap is widening — and it won't close with more headcount.
Pipeline deployment
All 8 agents configured for your tech stack, CI/CD, and existing tooling — Jira, GitHub, Slack, Confluence — integrated and running within weeks, not months.
5-domain test coverage
Functional, performance, visual regression, security, and chaos engineering — all covered from day one. Not just functional testing with a roadmap to 'maybe later.
Self-healing infrastructure
Tests that break on UI changes fix themselves in under 3 minutes. No maintenance backlog. No stale suite quietly giving you false confidence.
DORA reporting dashboard
Real-time quality metrics mapped to deployment frequency, lead time, change failure rate, and MTTR — in the language your leadership team already uses.
See what agentic QA looks like on your codebase.
We map your current QA setup to the pipeline, identify the highest-impact gaps, and model the cost and DORA outcomes for your team and scale.
