Financial institutions increasingly need to evaluate agentic AI systems before production, but AI governance frameworks are usually applied as static checklists, which are harder to envision how the AI governance framework would be applied in real financial institutional environment.

This talk introduces a prototype agent-based modeling environment that translates FINOS AI Governance Framework risks and mitigations into configurable simulation experiments. Users can define a financial-service workflow, select AI agents and human roles, enter deployment context such as model version, hardware, software, data access, orchestration, and approval rules, then choose risks and mitigation controls to test. The simulator runs counterfactual scenarios, such as data drift, authorization bypass, model misalignment, infrastructure failure, or compromised tools, and compares outcomes including policy violations, detection time, operational delay, human workload, customer harm, and resilience. The goal is to demonstrate how “governance as simulation” could complement governance as documentation and governance as code, while creating a foundation for a future open-source FINOS project.