The emergence of AI agents, which autonomously execute tasks with minimal human intervention, is transforming software economics by providing businesses with increased efficiency and scalability. However, pricing these agents presents a challenge due to their unique value delivery, which traditional models based on headcount or access fail to capture. The complexity of AI agents, powered by large language models and integrated with external tools, introduces variability in costs and outcomes, complicating the establishment of a standard pricing model. Usage-based pricing, which ties costs to measurable activity, is gaining traction as it offers flexibility and aligns costs with consumption, though it requires clear, auditable metrics to maintain customer trust and prevent billing disputes. Companies are advised to experiment with hybrid models and leverage tools like Moesif for precise usage metering, allowing for transparent and adaptable pricing strategies that accommodate the unpredictable nature of AI workloads.