When the precedent hasn’t been set yet, we get to write it
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. 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'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.
