AI agentic workflows are becoming increasingly important as organizations scale their AI deployments to production environments. These workflows enable autonomous decision-making by agents that interact with each other and the environment, adapting to dynamic conditions and handling complex tasks. To ensure reliability and performance, agentic workflows require robust design considerations such as high-throughput communication protocols, fault tolerance mechanisms, and resource allocation strategies. Implementing these strategies can involve leveraging industry standards and research-backed approaches, exploring AI agent frameworks, and employing techniques like parallel execution optimization and neural architecture search. Additionally, autonomous evaluation systems are crucial for assessing accuracy and reliability, while real-time system monitoring and security measures are essential for maintaining the smooth operation of agentic workflows. Emerging solutions like Galileo offer comprehensive tools to address these challenges, empowering technical teams to deploy and maintain robust agent workflows with confidence.