Would you trust an AI audit trail that never says “not captured”?
Monday is when this problem stops being theoretical.
A team uses three assistants, two editors, and one of those “we’ll figure out the provenance later” workflows, and suddenly the question is not whether AI touched the code. The question is whether your audit trail can tell you what it actually saw.
That is the part I keep coming back to: a flat log is easy to build, but it is not honest enough for mixed-tool AI work. One assistant exposes prompt text, another only gives you metadata, another leaves you with an editor diff. If you flatten that into one record type, you have not built provenance. You have built confidence theater.
What I wanted LineageLens to do was simpler and stricter: make capture level explicit. If the system saw the whole prompt, say so. If it only saw metadata, say so. If it only saw the file diff, say so. If it saw nothing reliable, say that too.
That sounds like a small schema choice, but it changes the whole conversation. Suddenly the record is queryable without pretending every tool exposes the same evidence surface.
How are you handling partial AI capture today: one canonical schema, or separate logs per provider?
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Lineage Lens
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I'd trust the audit trail more if it clearly says what was not captured. I think for mixed AI workflows, a clean single log is not enough unless it shows the evidence level behind each record; full prompt, metadata only, file, diff or not captured. Otherwise it can look complete while still missing important context
Lineage Lens
@busra_seker1 That’s exactly the concern that pushed me toward explicit capture levels. A provenance system becomes much more trustworthy once it can honestly distinguish between “full prompt observed,” “metadata only,” “diff-based inference,” and “not reliably captured.”
Otherwise a unified log can accidentally imply completeness even when different providers expose radically different evidence surfaces underneath.