Agentic AI's real failure point isn't the model — it's the data pipeline. When agents act autonomously on corrupted data, output guardrails can't save you. Your data needs a constitution, not better prompts.

Karpathy says vibe coding is passé. The new term is "agentic engineering" — and for legal and product teams, the distinction is a governance question, not a branding one.

Google research shows AI models that simulate internal debates dramatically outperform those that reason in monologue. For governance teams, the implication is clear: if dissent drives accuracy, hiding the chain-of-thought undermines trust.

RAG didn't die — it got rebranded as "context engineering." Kinda...

AI agents are moving from retrieving data to building memories about users. Most privacy frameworks weren't designed for that shift — and the gap is widening fast.

What compliance teams haven't figured out yet is that they own this problem.

2026 isn't about new AI capabilities — it's about stabilizing the ones we already have. For product counsel, governance built on shifting tools is governance built on sand.

The authors suggest treating AI agents as "legal actors" — entities that bear duties — without granting them legal personhood.