Taher A. Ghaleb’s AgenTag study tested a text-only classifier against 33,580 pull requests from five coding agents and 6,618 written by humans, and found it could tell them apart almost entirely from PR descriptions and commit messages — a signal that persists even once explicit “Generated by” markers are stripped out. That leaves open source disclosure policies, 51% of which require declaring AI use inside that same text field, resting on the one part of a contribution any contributor can rewrite before submitting.
The hosts also work through what the study doesn’t claim: a January 2026 predecessor found real code-level signal for individual agents like Claude Code and Codex, and AgenTag’s own dataset only covers agents operating openly under their own accounts — leaving open how much of the “prose fingerprint” is AI writing style versus vendor template, and whether a rewritten description, the one evasion nobody tested, would defeat it entirely.