In the realm of multi-agent AI systems using the Agent Development Kit (ADK), choosing between sub-agents and agents as tools is critical. Agents function by processing inputs to achieve specific goals, but how they manage control and context distinguishes their use. Agents as tools are designed for isolated, discrete tasks. They encapsulate their logic and operate statelessly, meaning they receive all necessary information in a single request without retaining any session data. This makes them ideal for reusable functions like executing SQL queries or generating visualizations without concern for previous interactions. On the other hand, sub-agents are better suited for complex, multi-step processes where context matters. They operate within a shared context, allowing them to access the parent agent's conversation history and maintain state across interactions. This hierarchical structure enables sub-agents to manage intricate tasks, such as planning a detailed travel itinerary that requires ongoing user interaction and feedback. For instance, in a travel planning scenario, the main travel planner agent coordinates various sub-agents for flights, hotels, and itinerary generation, each handling specific aspects while sharing relevant context. The article provides practical use cases to illustrate these concepts. In a data analysis task, for example, a root agent directs the flow of operations, utilizing tools for simple tasks like SQL generation and execution, while delegating complex visualization to a sub-agent. In contrast, for a travel planning request, multiple sub-agents work collaboratively, maintaining state and context to deliver a cohesive itinerary. Understanding when to employ each type of agent can significantly enhance system efficiency and scalability, ultimately leading to more effective AI solutions.