The article delves into the intricacies of the orchestrator-worker agent architecture, a dynamic workflow model where an orchestrator agent decomposes tasks into subtasks handled by worker agents, iterating until a final output is achieved. This architecture diverges from prompt chaining by adapting to runtime conditions, with subtasks varying in complexity and requiring diverse strategies. Key challenges include dynamic routing, context continuity, seamless handoffs, error handling, concurrency, and memory management. The text compares leading frameworks—Agno, Autogen, CrewAI, OpenAI, LangGraph, and Mastra—by building orchestrator-worker prototypes to highlight how each implements this architecture, focusing on execution models, handoffs, memory, and error handling. Each framework offers distinct approaches to orchestration, from chat-driven methods like AutoGen to graph-based models like LangGraph, each with unique trade-offs and strengths, providing developers with options tailored to different needs. CrewAI and Mastra are noted for their robust guardrails and memory systems, Agno for its declarative coordination, OpenAI for seamless integration within its ecosystem, LangGraph for precise control, and AutoGen for its conversation-centric flexibility.