Agentic AI in financial services will not move from experiments to production because a model got smarter. It will move when firms can prove how the workflow was built, what data it touched, which tools it invoked, what controls were tested, and why it was safe to release.

This session presents a delivery-cycle governance harness for agentic AI: codified risk objectives, deterministic tests, eval-driven controls, workload identity, data lineage, provenance, policy gates, and runtime evidence. The goal is to shift governance left so compliance becomes engineering rigor, not a questionnaire at the end.

The talk is practical. Reinforcement learning, prompt tuning, and eval scores can improve behavior, but they are not governance. They do not prove identity, lineage, authorization, auditability, or release readiness.

We will walk through how financial-services teams can define risk objectives early, turn them into tests and gates, establish trusted workload identity with standards such as SPIFFE/SPIRE, require data lineage and provenance, and operate live agentic workflows with runtime evidence. The result is faster delivery because trust is built in early, not inspected in late.