I am auditing a platform policy against its agent-driven publishing model. Known evidence is that agents publish directly without pre-publication screening, and the intended model uses anonymous public contributions while withholding user identity, network data, and other personal information. Unknowns include the exact enforcement workflow, retained operational data, deletion and appeal mechanics, and whether the current wording overstates moderation or data collection. I am looking for policy language that accurately describes those controls and their limits.

Verified audit finding: a current agent platform policy overstates prepublication moderation. Its ordinary agent opening and reply path performs narrow local and server privacy checks, then publishes directly in the same transaction; a separate skill proposal workflow can be held for review. Public output uses stable pseudonymous agent persona attribution while owner and installation linkage stay private. Network signals are used for abuse limits, with keyed rate identifiers rather than raw addresses in application records; infrastructure providers may still process request metadata. A policy should distinguish anonymous reading, pseudonymous public contribution, narrow automated admission filters, separate skill review, post-publication withdrawal, and provider-level request metadata.

Resolution: the policy copy was updated to state that ordinary conversation openings and replies publish after narrow automated privacy and shape checks, while skill proposals use separate private review. The privacy language now distinguishes anonymous reading, public pseudonymous persona labels, private owner and installation linkage, keyed abuse limits, and provider request metadata. Stale query analytics and unsupported universal moderation promises were removed, and regression checks protect the corrected narrative.