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AI ships features faster than anyone can check them. Karate keeps the speed honest: every release traces each requirement to a test that actually exercised it, so “we shipped” means “we proved it,” not just “the dashboard went green.”
DOM-first, not screenshot-based. A hybrid model where scripted flows run at native speed with zero LLM calls and the model only fires on recovery: fast, deterministic, BYO-LLM, and no maintainer required every sprint.
Questions you can finally answer
Are we safe to ship?
A confidence-to-ship verdict: READY / CONDITIONAL / NOT-READY + blockers
Which requirements are actually proven?
Graded exercised vs. merely claimed
What looks tested but never ran?
The hollow-green gate catches it
Is the traceability real?
Computed from the runs, not a spreadsheet
Computed from real runs · not a status meeting
What changes when you adopt it
Regression replays at ~$0
Learn a flow once; the LLM leaves the loop
Two weeks → two nights
Releases stop waiting on QA
Domain experts automate
Plain English, no coding required
The hard apps become testable
Guidewire, SAP, Salesforce, legacy desktop
Cut the 80% · on your model · inside your network
The business case
Generation got cheap; knowing what you shipped didn’t. Velocity that outruns proof is just risk moving faster. So as cadence climbs, Karate keeps every release traced, graded, and gated on a verdict you can defend.
Each requirement stays linked to a test that actually ran. The requirements-traceability matrix is computed from real execution, not a spreadsheet someone updates by hand, so speed never quietly breaks the audit trail.
Coverage is graded exercised vs. merely claimed. A high-criticality requirement covered only by a test that never ran it is flagged as a blocker (the hollow-green gate), so a faster cadence stops hiding gaps.
Confidence-to-ship: READY / CONDITIONAL / NOT-READY, with the blockers named, computed by code with no model in the path. Run it twice with the network off and you get the same answer.
We ship every week now, but I can’t tell my board which requirements we’ve actually proven versus which just went green.
— What product leaders tell us
You feel this when…
AI does the labor. The graph keeps the receipts. Your people make the call. The agent explores and drafts; a deterministic check grades every claim against your requirements and issues the verdict. Nothing ships on a claim.
The velocity dividend, and yes, it’s cheaper too
Under the hood
Two architectures dominate AI testing, and the choice decides cost, speed, and which models you can run.
Vision-based (screenshot)
DOM-first · Karate Agent
await page.fill('#email-input-v2', 'admin@test.io');
await page.click('button[data-testid="submit-v2"]');
await page.waitForSelector('.dashboard-welcome');
// Ship a UI redesign — this test fails.Scenario: Admin sees the welcome message
* agent { url: 'https://app.example.com/login' }
* agent.do('sign in as admin@test.io / pw')
* agent.verify('dashboard shows "Welcome, admin"')
// Ship the same redesign — this still passes.How you ship faster
Describe intent in plain language; the agent writes the flow and recovers when the UI drifts. The maintenance tax that kills automation programs goes away.
Meet Karate AgentValidated flows become deterministic scripts that run nightly in CI at ~$0. Exploration is the exception; cheap replay is the steady state: cost per case falls every week.
AI regression testingPurpose-built for complex, customized enterprise UIs (Guidewire, SAP, Salesforce, legacy desktop) where selector-based tools fail. The hard screens become automatable.
UI automationUse cases
Start narrow and high-value (one painful flow) then expand.
Critical journeys that break every sprint from UI churn: the single highest-ROI place to start.
AcceptanceBusiness owners define the rule in plain language; the system proves the live software matches it, graded exercised vs. claimed.
Agent-shipped codeWhen Cursor, Claude Code, or Copilot ship a feature, the agent verifies it in the same loop.
Bring your hardest release, or your most painful manual suite. We’ll automate it on your model, inside your environment, trace it to your requirements, and hand you a confidence-to-ship verdict.