AI agents are intricate systems that operate beyond traditional large language model (LLM) query-response patterns by engaging in agentic workflows that involve reasoning, tool usage, oversight, and orchestration. These workflows necessitate modular deployment, independent scaling, and advanced orchestration to manage complex tasks and ensure reliable performance in production. The agentic stack comprises four layers: the cognitive layer, powered by state-of-the-art LLMs to interpret user intent; the tool interaction layer, which utilizes various tools to fulfill user needs; the oversight layer, ensuring safety and alignment; and the orchestration layer, coordinating interactions among multiple agents. Deploying these agents involves sophisticated infrastructure supporting component-specific autoscaling, heterogeneous hardware, fault isolation, and latency-aware routing to optimize performance and cost-efficiency. Evaluation of agents in production requires comprehensive analysis of their behavior and actions within dynamic environments to identify and address systemic issues, with tools like Patronus AI's TRAIL and Percival aiding in debugging and improving agent workflows. As AI agents become more complex, building and assessing them demands a robust stack encompassing models, infrastructure, and oversight to ensure effective real-world deployment.