Agentic AI and the Autonomous Financial Core: Architecting Cloud Platforms for Real-Time Decisioning and Risk Mitigation
Keywords:
Agentic AI, Monte Carlo Simulation, Conditional Value at Risk (CVaR), Financial Autonomy, Low-Latency Systems, Cloud-Native ArchitectureAbstract
Financial markets are extremely fast and complex where conventional predictive models of artificial intelligence are usually too slow and inflexible to deal with real-time risks. This study proposes the idea of the Autonomous financial core (AFC) which is a cloud-based architecture, which runs on Agentic AI and allows autonomous and goal oriented financial decision-making. The AFC is going to anticipate the market conditions, strategize and take decisions in real-time as opposed to the traditional AI systems where prediction occurs and the implementation chain requires a human operator.
The research will have a quantitative simulation-based approach to compare an Agentic AFC system to a classical predictive AI system given the same market conditions. Such performance measures as decision latency, decision accuracy, portfolio returns, risk measures, and system stability are used to assess the performance. The agent-based market models and Monte Carlo simulations are employed to make the conclusions robust and repeatable. Their results indicate that AFC has a substantial effect on decision latency, enhanced performance on returns in volatile conditions, reduced downside risk assessed by Value at risk and Conditional Value at risk and exhibiting more consistent results in repeated simulations. The results indicate that agentic architectures offer a realistic and scalable base of real-time decisioning and risk mitigation in the contemporary financial systems.
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