AI agent orchestration frameworks offer an efficient approach to managing complex workflows by coordinating multiple specialized agents, each adept in a specific domain, as opposed to overburdening a single agent with multiple tasks. Key components of these frameworks include state management, communication protocols, orchestration patterns, tool integration, and error recovery, all of which ensure seamless task handoffs and context preservation across agents. The guide examines 11 different frameworks, categorized into visual and low-code tools, code-first SDKs, and enterprise infrastructure platforms, each catering to distinct needs ranging from business automation to advanced developer control and enterprise-scale deployment. These frameworks allow for task specialization, cost efficiency, scalability, and easier maintenance, ultimately enhancing workflow reliability and performance in production environments. The analysis highlights the strengths and limitations of each framework, considering factors such as vendor dependency and integration capabilities, to help users choose the most suitable solution for their specific requirements.