Legal AI vendors should publish what they refuse to build, not just what they ship. Architectural constraints aren't limitations — they're competitive differentiators. The first privilege breach will prove who got this right.

When long-running AI agents summarize their own context to stay within token limits, they're deciding what to forget. That's not an engineering problem — it's a governance one.

Better context beats a better model — which means AI risk governance needs to shift from the model layer to the retrieval layer. That's where defensibility lives now.

LLMs break traditional observability — and that creates a compliance gap most governance teams haven't addressed yet. If you can't trace the full AI pipeline, you can't audit it.

You wouldn't tell a first-year associate "do law" and expect good results. So why are attorneys doing exactly that with AI agents? Dan…

The trajectory is encouraging — the most capable models performed best. But 20 percent is not a foundation for compliance frameworks.

SaiKrishna Koorapati's piece in VentureBeat makes the case that observable AI isn't about adding monitoring dashboards. It's about audit trails that connect every AI decision back to its prompt, policy, and outcome

The accountability gap doesn't just create compliance risk. It creates operational security risk. When model developers point to deployers and deployers point to model developers, the space between them becomes the attack surface.