This paper introduces Federated Agent Optimization (FAO), a framework for distributed agents to collaboratively improve by exchanging controlled information while keeping private knowledge local. FAO balances agent utility, privacy leakage, and communication cost. It abstracts, protects, aggregates, and adapts private experience into transferable capabilities, enabling agents to benefit from each other without direct experience sharing.