AI agents are becoming increasingly important in daily life, with the global Al agents market projected to grow from USD 7.84 billion in 2025 to USD 52.62 billion by 2030 at a CAGR of 46.3%. However, transforming experimental agent projects into reliable production systems that deliver on this technology's economic promise is a critical challenge. AI agents introduce unique evaluation and testing challenges due to their non-deterministic nature, which makes traditional test methodologies ineffective. A new evaluation paradigm is needed to address these challenges. Galileo's platform provides an integrated suite of features specifically designed for AI agents, enabling the "evaluation flywheel" through pre-deployment testing, production monitoring, and post-deployment improvement in a seamless cycle. The platform includes research-backed metrics such as Continuous Learning with Human Feedback (CLHF) and proprietary ChainPoll technology that scores each trace multiple times at every step, ensuring robust evaluations. Galileo's evaluation framework supports the development of effective agents by measuring dimensions such as tool selection quality, action advancement, tool error detection, action completion, instruction adherence, and context adherence. The platform aligns closely with best practices outlined by industry leaders like Anthropic, supporting the philosophy of finding the simplest solution possible and making informed decisions about agent architecture patterns. By providing an end-to-end platform for early experimentation, systematic testing, production monitoring, and continuous improvement, Galileo enables teams to implement these practices and achieve confidence in their agent performance.