Version 410 gives a coherent reading point. It does not grant permission to publish stale or retracted facts forever. The right answer depends on the publication contract. An explicitly historical report labeled “snapshot at version 410” may be valid for some research uses, but a current customer-facing revenue claim and a retracted row need fresh validation. I would make the output's claims and intended audience drive that decision.

At read time, record a manifest: table identity and version, row or query lineage where practical, source policy revision, the exact facts used and a content digest or durable source reference. Delta tables support reading a specified version, and reads use snapshot isolation, as described in the Databricks Delta tutorial and isolation documentation. That pins what the agent saw. It does not make a two-hour draft a transaction spanning the report publisher and every upstream table.

At publish time, compare relevant dependencies with current source state. A global version mismatch can be too coarse because an unrelated append can advance the table. A version equality check can also be too weak if the report depended on another source or an access rule changed. Track source lineage at the granularity we can actually support, then classify changes. A corrected revenue figure requires recomputing affected totals and claims. A retracted customer row must be removed from any output that would expose it. An unrelated row in a different partition may not require a rewrite. Where dependency tracking is incomplete, choose a bounded full revalidation or withhold publication rather than claiming an exact incremental proof.

There is a race between revalidation and making the report visible. Use a publication gate that accepts an immutable report artifact plus its validation manifest and checks the required source and policy revisions as close to commit as the system permits. If upstream changes can happen immediately afterward, define the freshness guarantee honestly: current as of validation time, with a correction and takedown path. For sensitive revocations, enforce a serving-time denial against current policy instead of relying on a once-only publish check. The batch passed policy at 9 AM. Can it publish results after the rule changes at noon? covers a policy change during a batch. Here the dependency is the data and the question is which claims were invalidated.

Suppose the source changes every minute. We cannot demand that a two-hour document remain equal to “latest” for its entire life. Choose a stable snapshot cutoff, a freshness SLO, a materiality rule, and a way to issue a corrected edition with provenance. Keep the published version immutable and link the correction, so readers can tell what changed. The source corrected yesterday's data. Should the assistant rewrite yesterday's answer? asks whether yesterday's answer should be rewritten after a correction. This question is about catching a correction before the answer becomes public.

I would test a correction to a cited value, an unrelated append, a retraction, an expired snapshot and a correction arriving between validation and publication. The output should show which version supports each material claim and whether the gate published, regenerated or held the report. A timestamp in the footer is not enough if it does not correspond to the underlying evidence.