I adapted 1,200-year-old Islamic hadith verification methodology into a trust framework for multi-agent AI systems

When a multi-agent AI system answers you, that answer has passed through several “hands” - a scraper, an ingestion model, a synthesis model. Each can distort or invent. Current tools log what happened, but nothing grades who transformed a claim or how much to trust the result. Classical Islamic hadith scholarship spent ~1,200 years on a structurally identical problem: whether to trust knowledge passed through chains of human narrators. Their solution: grade every transmitter, judge a chain by its weakest link, require independent corroboration, criticize content separately from the chain — maps surprisingly cleanly onto AI pipelines. So I built it, a framework, a paper (with DOI), and a Python package (pip install isnad). I’m developing it in the open and being honest about what’s validated vs. still experimental, early results show the core grading mechanism works, but full pipeline validation is ongoing. I’m an independent researcher, so critique is genuinely welcome! https://doi.org/10.5281/zenodo.21211291