Bounded-Pass Framework Aims to Fix LLM Pipeline Debugging Weeks After Failures

A developer has spent several months building the Bounded-Pass Framework, a structured approach designed to make LLM-driven pipelines auditable and recoverable after failures. Most current AI systems only preserve final outputs or conversation transcripts, making it difficult to trace exactly which step, context, or policy version caused a production error. The framework organizes work into a directed acyclic graph of single-concern tasks, where each pass receives only its relevant dependencies and outputs are logged as immutable events. Independent verification gates evaluate outputs against external criteria, preventing the model from altering rules to validate its own results. The specification is publicly available and is intended as a base that teams can extend with their own schemas, domain constraints, and storage infrastructure.