FORMALIZING THE CONSTRAINT SATISFACTION SCORE (FCSS) FOR SAFETY-CRITICAL AGENTIC FINANCIAL PLANNING

The transition from Generative AI to Agentic AI is an improvement in systemic risk. While the output of Generative AI is designed to be interpreted by humans, Agentic AI is capable of multi-step planning with real world financial implications. The current set of evaluation metrics, based on token overlap metrics such as bleu and rouge, is fundamentally inadequate to evaluate the reliability of agent-based techniques in high-stakes personal finance decision-making, where failure to comply with even one constraint-such as liquidating the emergency fund in response to a market event-has catastrophic implications. We propose the Financial Constraint Satisfaction Score (fcss) as a novel deterministic engineering metric based on the Satisfactory Budget Division model. Our proposed metric is built on a hard constraint on the satisfaction threshold τ and uses a Calibration Decision Loss (cdl) term to address over-confident planning in low-data regimes. Our empirical evaluation using the classic benchmarking framework shows that the Hierarchical Supervisor-Worker Multi-Agent System (MAS) architecture outperforms single agent baselines in terms of positioning on the Cost-Accuracy Pareto Frontier, while providing guarantees on Strategic Alignment and the Maintenance of Safety Boundaries.