Small and medium-sized enterprises (SMEs) face persistent challenges in financial decision-making due to volatile cash flows, limited planning capacity, and constrained access to real-time analytical tools. Traditional enterprise financial management systems are often rigid, centralized, and cost-prohibitive, making them unsuitable for the dynamic operational environments characterizing SMEs. To address these constraints, this paper proposes a cloud-native, self-optimizing autonomous finance system informed by the principles of Agentic AI, where multi-agent reinforcement learning (MARL) enables adaptive financial intelligence across distributed business processes. The system architecture is designed to function as a coordinated ecosystem of specialized agents responsible for core financial tasks such as liquidity forecasting, risk scoring, expenditure optimization, and capital allocation. Each agent learns from streaming operational data, interacts with other agents to share state signals, and continuously improves its policies through reward mechanisms tied to financial stability, cost efficiency, and risk minimization. At the macro level, the platform provides SMEs with a real-time, transparent financial cockpit hosted on scalable cloud infrastructure, reducing overhead while ensuring elastic compute scaling aligned with workload demands. At the micro level, reinforcement learning enables granular behavioral adaptation for example, automatically adjusting credit utilization strategies or supply-chain payment scheduling in response to market signals. The system’s distributed design prevents single-point failure, enhances resilience, and allows incremental agent deployment based on organizational maturity. The outcome is a financial management paradigm that transitions SMEs from reactive, spreadsheet-driven decision-making to proactive, autonomous optimization. This approach democratizes access to advanced financial intelligence and positions SMEs to operate with the strategic agility typically reserved for large enterprises.