Compliance used to mean documentation. For AI agents, it means something else entirely
Governance needs to be in the architecture conversation, not the incident response.
Microsoft's Argos and the verification layer AI agents actually need
The framework trains AI agents to be right for the right reasons — not just right by coincidence. For AI governance, that distinction is everything.
AI agents fail because they don't know what "customer" means at your company
AI agents fail because nobody defined what "customer" means in your business. Ontology infrastructure provides semantic guardrails that technical controls alone can't deliver.
Not all AI agents carry the same legal risk
Not all AI agents carry the same legal risk. Your governance framework should distinguish between reflex agents, learning agents, and multi-agent systems — because the liability profile is fundamentally different. https://www.databricks.com/blog/types-ai-agents-definitions-roles-and-examples
Agentic AI doesn't need better models — it needs a data constitution
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.
The vibe coding guy says vibe coding is passé
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.
AI models that debate themselves are dramatically more accurate
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 isn't dead, it just got rebranded as context engineering
RAG didn't die — it got rebranded as "context engineering." Kinda...
