Policy-Driven Agentic Frameworks for Financial AI: Lessons from Portfolio Construction and CVA Risk Management

Abstract

Modern financial AI increasingly relies on agentic architectures for reasoning, adaptive decision-making, and feedback, yet few frameworks treat governance, determinism, and observability as first-class design principles. We introduce a unified policy-driven framework that embeds a deterministic orchestrator, a declarative policy engine, and an observability layer to ensure traceable, auditable, and controllable behavior. The framework is instantiated through two complementary implementations: Agentic Black–Litterman (ABL) for ex-ante portfolio optimization and Agentic CVA (ACVA) for ex-post counterparty-risk valuation. Both implementations share the same meta-architecture and are evaluated under real stress episodes, including the 2008 Global Financial Crisis and the 2023 Banking Turmoil. Results show improved robustness, control, and explainability under extreme volatility, validating policy-governed orchestration for mission-critical financial systems. We discuss engineering lessons on native policy governance, deterministic control, and observability, and outline directions for broader deployment of governed autonomy in financial AI.