When the same AI edit means different things in different places
by•
One thing that surfaced while tightening LineageLens this week: capture is not the hard part. Agreement is.
If the extension, backend, and MCP server describe the same AI edit with slightly different field names or status values, you do not have provenance, you have three believable stories about the same event. That matters because reviewers and assistants start trusting whichever surface they looked at last.
The question I keep coming back to is simple: if a record can look applied in one place and accepted in another, is that still a single source of truth?
316 views
Replies
Crazy how a tiny status mismatch can turn one AI action into three different stories across the stack.
Lineage Lens
@joseph_walker2 That’s the part that feels deceptively dangerous. The underlying event never changed — only the interpretation did — yet suddenly the extension says “applied,” the backend says “accepted,” and another surface treats it as “pending.”
Same action. Multiple realities.
Really interesting point , consistency across layers feels like the real challenge here, not just tracking the edit itself.
Lineage Lens
@evelyn_white Exactly. Capturing the edit is mostly an infrastructure problem. Keeping every layer semantically aligned over time is the harder systems problem.
Once different surfaces start inventing their own lifecycle meanings, provenance stops being deterministic and becomes interpretive.
Yes, I’d still treat it as one source only if the event has a canonical state machine behind it and every surface renders from that, instead of mapping its own labels.
In practice I’d store the immutable edit event plus a normalized lifecycle field like proposed -> applied -> accepted/rejected, then let the extension/backend/MCP carry local display labels as metadata. The moment two surfaces can independently translate status, reviewers are no longer auditing provenance; they’re auditing adapters.
Lineage Lens
@danielsinewe I’m increasingly convinced provenance systems fail less from missing data and more from semantic divergence. Capturing events is easy compared to forcing every surface to agree on lifecycle meaning over time.
That's exactly where systems quietly break down , once semantics drift , the "record" stops being an anchor and becomes a UI dependent interpretation . At that point, provenance is less about capture and more about enforcing a shared meaning layer across every surface that reads it.
This is something I think about a lot. We ran into this exact issue when deploying AI agents across different departments. Same prompt, same model, but the output meant completely different things to the legal team vs the marketing team. A "summarize this" instruction on a contract is a compliance task.
The same instruction on a blog draft is a creative task. Context changes everything, and most tools just ignore that. Curious how Lineage Lens approaches tracking those differences, because right now we're basically doing it manually and it's not scaling.
Lineage Lens
@nolan_vu This is very close to the problem space I’m thinking about. The hard part is that provenance is not only about what happened, but also the operational context around it. The same model action can carry completely different risk, review, and compliance meaning depending on where it was used and who consumed it.
One direction I’m exploring with LineageLens is treating lifecycle state + execution context as part of the canonical event itself, instead of leaving interpretation to each downstream surface. Otherwise every department ends up reconstructing intent differently from the same raw trace — which sounds a lot like the scaling problem you’re describing.
@praveen62 Agree 100%, especially the part about departments reconstructing intent differently from the same trace. We've seen this firsthand. Engineering looks at a model output and sees a performance metric. Compliance looks at the same output and sees a risk event. Nobody's wrong, they're just reading the same data with completely different lenses.
Embedding context directly into the event makes way more sense than expecting every team to figure it out after the fact. The question I'd push on though: how do you decide which context matters at capture time? Because over-indexing on metadata upfront can slow things down, but under-indexing means you're back to guessing downstream. Where's that line for you?
This make sense. An edit history is only helpful if everyone can trust that they are looking at the same version of events. If te extension backend and MCP layer describe the same schange differently, the audit trail can become confusing instead of useful
Lineage Lens
@busra_seker1 Exactly. An audit trail only works if every layer is describing the same reality consistently. Otherwise reviewers are forced to reconcile competing interpretations instead of verifying a canonical history.
That’s the line where provenance shifts from clarity into ambiguity — even when all the underlying data technically exists.
This highlights a key gap in most AI systems today , capture is easy , but consistent meaning across layers is what actually determines trust in the record.
Funny thing is humans already work like this. In relationships, if two people describe the same event differently long enough, trust starts collapsing and people believe whichever version feels emotionally safer in the moment.
AI provenance feels like we're rebuilding that same problem at machine scale.
Lineage Lens
@trish_maine That’s actually a really good analogy. Provenance drift feels less like a database problem and more like narrative fragmentation — once different surfaces repeatedly describe the same event differently, consumers stop trusting the shared reality and start trusting whichever interpretation feels most coherent in context.
The scary part with AI systems is that machines can scale that ambiguity far faster than humans ever could.
Feels like AI is shifting the challenge from “can we generate this?” to “can we trust and trace where it came from?”
Lineage Lens
@kamrankhan I think that’s exactly the transition happening right now. Generation quality is improving fast enough that the harder question is becoming provenance: where did this come from, what actually happened to it, and can every system involved describe that history consistently?
The bottleneck is shifting from creation to trust infrastructure.
Lineage Lens
Drop any questions below! Happy to nerd out.