The enterprise question isn’t capture. It’s control.

@lucy_bennett1 That’s honestly much closer to how I’ve started thinking about it too. The deeper I go into provenance and governance, the less it feels like a traditional AI coding assistant problem and the more it resembles audit infrastructure for autonomous systems.

The difficult part is not generation anymore — it’s creating a trustworthy operational history around generation.

@sanjau_sanjay Honestly, keeping lifecycle semantics consistent across layers has been one of the hardest parts so far. Capturing events is relatively straightforward compared to making sure the extension, backend, and policy layer all describe the exact same event state in a deterministic way.

Once those meanings drift even slightly, trust in the provenance chain starts degrading surprisingly fast.

@tessa_lynch I think smaller teams care later, but they eventually run into the same problems once AI usage becomes shared instead of individual. Early on, speed matters most. But the moment multiple developers, agents, or workflows start interacting with the same AI-generated changes, questions around ownership, review, and traceability appear surprisingly quickly.

The scale is smaller, but the coordination problem is very similar.

@carolina_ellen In practice, permissions usually matter first because they define the immediate trust boundary around the records. Teams want to know who can view, approve, export, or modify provenance data before thinking about long-term storage rules.

But retention becomes critical once organizations start treating those records as operational or compliance evidence instead of temporary telemetry.

@keesan12 That boundary is one of the harder problems I’ve been thinking about. My current view is that retries should not automatically inherit legitimacy from the original event or get treated as entirely independent actions either.

If a policy blocks a change, the retry path needs provenance continuity: what changed between attempts, whether the prompt/model/context materially shifted, who initiated the retry, and whether the policy conflict was actually resolved versus merely rephrased around.

Otherwise governance systems end up tracking retries as disconnected events while developers experience them as the same operational intent.

@busra_seker1 That ordering makes a lot of sense honestly. Once teams begin storing prompts, generated outputs, and workflow decisions, retention and review quickly become operational concerns rather than optional governance layers.

I also think your point about workflow friction is critical. Policies only work long term if developers can realistically follow them inside normal engineering flow instead of treating them as separate compliance overhead.

@isabella_moore1 Occasionally, yes — especially in industries where records themselves become compliance artifacts very quickly. Teams handling regulated workflows sometimes worry about retention first because they need guarantees around deletion schedules, audit windows, and legal hold behavior before they are comfortable storing provenance centrally.

But in most enterprise environments, SSO still tends to arrive first because identity is usually the prerequisite for trusting any governance layer at all.

@henry_lindsey Exactly. Visibility scales relatively easily because it’s mostly an observation problem. Control is harder because it requires consistent enforcement across identity, retention, permissions, policy evaluation, and workflow state — often across multiple systems at once.

That’s usually where AI governance stops being a logging problem and starts becoming infrastructure design.