Dev Grover

Been thinking about this after something that came up recently. Imagine an AI agent makes a recommendation that ends up influencing a customer workflow. The recommendation gets reviewed, approved, and eventually becomes part of how the team operates. Fast forward a few months and someone wants to understand why that decision was made.
The interesting part is that the technical history is usually still available. You can find the output. You can find the prompt. You can usually figure out which model generated it. What can be surprisingly difficult to find is the human context around the decision. Who reviewed the recommendation? Who approved it? What information did they have that made the recommendation seem reasonable at the time?
The more AI becomes part of everyday workflows, the more I find myself paying attention to that layer. Understanding the output matters, but understanding why someone trusted that output often matters just as much. A lot of conversations around AI accountability focus on the model. I suspect a lot of the missing context lives around the people making decisions with it.
Curious how your team is keeping track of that today, lets discuss it below...

Something I've been thinking about recently:
Let's say an AI agent makes a recommendation and that recommendation ends up influencing a real business decision. A few months later, someone wants to understand why that decision was made. In most cases, it's not that hard to find the model, the prompt, or the output. Teams are getting much better at tracking those things.
What feels harder is understanding what happened in between. Who reviewed the recommendation? Who approved it? What information were they looking at when they decided to move forward with it?
Maybe there was a conversation that wasn't documented. Maybe there was context that seemed obvious at the time but wasn't recorded anywhere. Maybe there were reasons for trusting the recommendation that never made it into a system.
I keep coming back to this because the output is only one part of the story. The decision happens when a person looks at that output and decides what to do next. If that context disappears, it becomes much harder to understand how a decision was made, even when the AI history is still available.
Curious how other teams are thinking about this.

I think most AI governance conversations stop too early.

Teams talk about dashboards, usage charts, and prompt capture. Those are useful, but they are not the same thing as a trustworthy record.

The harder problem is this: if someone asks you six months later whether a block of code was AI-generated, can you prove the record still means what it said when it was created?

That is why we added two things in LineageLens: a provenance hash chain and a signed AI BOM export.