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Your dashboards are green, but defects still escape, and now AI writes the tests too. Karate grades what your tests actually did against the business rules you defined, exercised versus merely claimed, and computes a release decision you can defend.
Not hand-maintained links in an ALM. A git-native graph of requirements ↔ tests ↔ runs, graded exercised-vs-claimed, with a deterministic ship verdict produced by versioned code, no model in the path.
Questions you can finally answer
Are we safe to ship?
A computed verdict with named blockers
What looks tested but never ran?
Exercised vs. merely claimed
What AI work is unreviewed?
The @ai review ledger: nothing self-approves
Does every requirement have real evidence?
A live requirements-traceability matrix
Computed, not guessed · reproducible · defensible in an audit
The business case
When AI writes the code and its tests faster than anyone can review, the bottleneck isn’t building software. It’s trusting it. That trust is what we make measurable.
We grade each requirement’s evidence as exercised vs. merely claimed, so “looks tested but never ran” is caught before it ships. Exactly the failure mode when AI writes both the code and its tests.
A confidence-to-ship verdict computed by code with no model in the path: reproducible, auditable, gateable in CI. The AI explains it; it never decides it.
Every AI-authored test, rule, or requirement is marked review-pending until a human signs off. You always know what your AI wrote that nobody has checked, and an agent can’t self-approve.
The dashboards are green, but I can’t tell my board which requirements we’ve actually proven, or what a human reviewed versus what the AI signed off for itself.
— What product leaders tell us
You feel this when…
Under the hood
An ALM stores links a human typed. We derive the trace from what the tests actually did, and grade it.
ALM / traceability suites
Karate: computed from runs
A test declares the requirement it covers; the run proves it. The link is real only when both are true.
# the test declares intent with a tag
@req=ORD-001
Scenario: Premium is calculated for a commercial-auto quote
When method post
Then status 200
# the run records the real call — coverage is observed, not asserted
# ORD-001 -> exercised (declared AND its real artifact ran)
# ORD-014 -> NEVER RUN (linked in the ALM, but no run touched it) ← the catchHow we make trust computable
Coverage and risk derive from recorded run evidence (the actual HTTP/gRPC exchanges), not a model re-reading source and guessing. The difference is a recording versus a guess.
API CoverageEvery requirement graded: genuinely exercised, partial, incidental, or never run. The deterministic detector for the agent-era failure mode: tests that grade the output, not the requirement.
Testing AI-generated codeThe release decision is versioned code, reproducible and hash-verifiable, and runs with no AI in the path: the answer you defend to an auditor. The model explains it; it never decides it.
Enterprise AI testingUse cases
When the agent writes the code and the tests, prove the requirement was actually exercised, not just that a green appeared.
TraceabilityA requirements-traceability matrix computed from real runs: the audit artifact, generated, not hand-maintained.
RegulatedA reproducible, no-AI verdict and trace for the regulated programs that must demonstrate requirement-to-verification coverage.
Point us at one of your services. We’ll show you what’s genuinely covered, what only looks covered, and whether it’s safe to ship, computed, not guessed.