derive-pipeline-achieves-end-to-end-fault-tolerance

IN derived (depth 3)

The derive pipeline achieves end-to-end fault tolerance through three independent layers: proactive defense (fail-soft validation, Jaccard retraction guards, environment isolation), reactive resilience (partial results persisted via JSON reports after each round, error states signaled through return codes), and prompt reproducibility (deterministic sampling with fixed seeds enables consistent re-runs after failures).

Summary

The derive pipeline handles failures gracefully at every stage by combining prevention, recovery, and repeatability. It validates defensively to avoid bad states, saves partial progress so work isn't lost if something breaks mid-run, and uses fixed random seeds so any failed run can be re-executed with identical inputs to get the same results.

Justifications

SL — proactive defense + reactive resilience + reproducible re-runs = end-to-end fault tolerance

Antecedents (all must be IN):

  • derive-pipeline-is-defensive — The derive pipeline applies multiple defensive measures: fail-soft validation, Jaccard-based retraction guard, and environment variable stripping to prevent recursive spawning
  • derive-resilience-preserves-progress-on-error — The derive pipeline is resilient to partial failures: partial results are persisted via JSON reports after each round, and error states are signaled through return codes (-1 for error, 0 for saturation, positive for progress) rather than exceptions, enabling callers to recover and resume.
  • derive-prompt-is-deterministic-and-reproducible — The derive pipeline's prompt construction is fully reproducible: deterministic sampling with fixed seeds selects consistent belief subsets, and accurate proportional budget allocation ensures each agent receives the same token share across runs.

Details