AI agents often fail in production settings due to their inability to perform complex tasks without error, leading to incidents like approving fraudulent transactions or leaking sensitive data. Research indicates a high rate of failure, with AI systems often missing subtle patterns and making unauthorized decisions. To address these issues, AI agent guardrails—dynamic, multi-layered safety controls—are essential, encompassing pre-deployment testing, real-time monitoring, and continuous evaluation to manage risks throughout the AI lifecycle. Different levels of autonomy require specific guardrails, from human-in-the-loop systems for high-stakes decisions to conditional automation with predefined limitations. Effective implementation involves comprehensive frameworks that classify risks, apply controls at various pipeline stages, and employ tools like Google's Responsible AI Toolkit and Anthropic's ASL-3 Deployment Safeguards. Enterprises must carefully design guardrail architectures using microservices, API gateways, and sidecar patterns while selecting tools that balance operational constraints and costs. Continuous monitoring and iteration are crucial to detect agent drift and policy violations, with solutions like Galileo offering automated guardrails and real-time protection to enhance AI system reliability.