Regulated enterprises already run critical JVM services. Their challenge is adding confidential AI controls, auditable guardrails, and secure workflow boundaries without replatforming to Python. Using LLM4S as an open-source reference implementation, this talk separates application-layer concerns, including orchestration, prompt-injection defense, PII handling, retrieval grounding, and agent handoffs, from infrastructure-layer controls such as attested TEEs, isolated inference, secret release, and policy enforcement. Attendees leave with a practical blueprint for privacy-preserving, production-oriented agent workflows on top of existing JVM estates.