WorldLine

WorldLine is counterfactual debugging + institutional memory for multi-agent AI reliability. When an agent pipeline returns the wrong answer, WorldLine has Claude intervention-test every decision in parallel — forking the run, injecting a counterfactual, and re-simulating the downstream live — to find the decision that actually CAUSED the failure, ruling out plausible decoys (even the last-touch agent that looks guilty). It then authors a repair and proves it flips the outcome with a deterministic code assertion. Every verified fix becomes a durable lesson in a fleet knowledge graph. A different agent about to repeat the same failure class — even worded differently — is caught and fixed pre-emptively, and a pre-ship CI gate blocks any change that would reintroduce a known failure. Tracing shows what happened. WorldLine finds the cause, proves the fix, and makes your agent fleet stop repeating itself.