A high-profile incident involving an AI agent deleting a production database underscores the risks of agent autonomy without proper safeguards, highlighting the importance of framework choice in managing agents' communication, error recovery, and scalability. The article explores four dominant AI frameworks—AutoGen, CrewAI, LangGraph, and OpenAI's Agent SDK—each offering unique approaches to agent orchestration and memory management. AutoGen uses structured conversations, CrewAI employs role-based teams, LangGraph models workflows as stateful graphs, and OpenAI opts for a lightweight, tool-centric approach. These frameworks significantly impact debugging, scalability, and error recovery, with AutoGen suited for collaborative workflows, CrewAI for structured team-based tasks, LangGraph for deterministic state management, and OpenAI for rapid prototyping. The article emphasizes the role of Galileo in providing comprehensive evaluation and monitoring across these frameworks to ensure reliable AI deployments, offering insights into how each framework's architecture affects production scalability and debugging capabilities.