WorldLine forks a failed multi-agent run at the decision that caused it, re-simulates the future live, proves the repair — then files it as fleet memory so the same failure never ships twice. Tracing shows what happened; WorldLine makes your agents stop repeating it.
The same input yields different paths, so failures are hard to reproduce and harder to attribute. When a 7-step agent pipeline returns the wrong answer, which decision broke it? Today you read traces and guess. Replay and forking exist — but you still pick the checkpoint, and nothing proves the fix.
Every intervention-tested fix becomes a durable lesson your whole fleet inherits. When any agent — even a different one — is about to repeat a known failure class, WorldLine catches it from memory and applies the verified fix before it ships. Observability shows what happened; this makes your agents get more reliable over time.
A multi-agent run returns the wrong outcome. WorldLine treats the failure as a signal, not a dead end.
Claude intervention-tests every decision in parallel — only the one whose correction flips the outcome is the culprit, even when last-touch blame points elsewhere.
Claude (effort=max) explains the root cause and rewrites the offending prompt or policy.
The full workflow re-runs with the patch; a code assertion proves the outcome flipped.
The verified fix becomes a durable lesson in fleet memory — failure class, root cause, proof, and the agents it protects.
Any agent — even a different one — about to repeat that failure class is caught from memory and fixed before it ships.
Show, repeat, or explore one alternate path — but you still pick the checkpoint, and nothing proves the fix.
LangGraph time-travel (updateState+ resume) and AgentOps already support replay and forking from checkpoints — we don't claim otherwise. WorldLine's wedge is the autonomous loop on top: intervention-tested attribution → repair → verification.
Hit re-run and the whole loop — audit, fork, repair, verify — recomputes on the deployed backend.