Autonomous AI agents face significant challenges in achieving production-grade reliability, with a current success rate of only 50% in common workflows due to issues such as security lapses, hallucinations, memory poisoning, and planning loops. To address these challenges, implementing robust guardrails is crucial. This involves translating policies into machine-verifiable controls, deploying comprehensive metrics for monitoring, enforcing role-based access controls to prevent privilege escalation, and clustering similar failures to expedite root cause analysis. Platforms like Galileo facilitate these processes by integrating automated quality guardrails into CI/CD workflows, deploying multi-dimensional response evaluations, offering real-time runtime protection, and enabling human-in-the-loop optimization through continuous learning. This integrated approach not only enhances the reliability of AI agents but also reduces the cost of evaluation, ensuring compliance and building trust with users.