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Module 19: Collaborative Teams - Using Modes and Hand-offs

Theory​

So far, you have learned how to define a graph structure from the outside. But what if the nodes themselves could decide how to collaborate?

ADK 2.0 introduces Collaboration Modes and Native Hand-offs to create fluid, self-managing agent teams.

1. Collaboration Modes​

When you add a sub-agent to an Agent or Workflow, you can specify its mode. This setting controls how the sub-agent behaves and how it returns control to the parent.

  • chat (Default): Full multi-turn interaction. The sub-agent keeps control until it explicitly performs another transfer.
  • task: The sub-agent can ask the user questions but automatically returns control to the parent once its task is complete.
  • single_turn: No user interaction allowed. The sub-agent performs a single reasoning step and returns the result immediately.

2. The Power of task Mode​

In ADK 1.x, you often had to prompt your sub-agents to "hand back control." In ADK 2.0, using mode="task", this is handled by the framework.

from google.adk import Agent

# Specialist in 'task' mode
researcher = Agent(
    name="researcher",
    mode="task", # 🔄 Automatic return to parent!
    rerun_on_resume=True, # Required whenever a node can pause/resume across turns
    instruction="Research the topic and provide a summary."
)

# Coordinator
root_agent = Agent(
    name="coordinator",
    rerun_on_resume=True, # Required: the coordinator is part of the same dispatch chain
    sub_agents=[researcher], # Framework auto-injects 'request_task_researcher' tool
    instruction="Delegate research to the specialist, then write a conclusion."
)

3. Agent Transfer (The "Hand-off")​

Even without modes, an agent can decide to hand over the entire conversation to another node in the graph. This is the Agent Transfer pattern.

The framework automatically injects tools like request_task_<agent_name> into the parent's toolkit based on the sub_agents list. The LLM then "calls" these tools to perform the hand-off.

Why use Collaborative Teams?​

  • Predictability: You know exactly when and how control will flow back to your main orchestrator.
  • Natural Conversation: The transition is seamless for the user, as the context is preserved across nodes.
  • Cleaner Prompts: You no longer need to write complex instructions about "how to return control."

Key Takeaways​

  • Collaboration Modes (chat, task, single_turn) manage sub-agent lifecycle.
  • mode="task" is the standard for delegation where you want the specialist to finish and "come back" to the main flow.
  • The framework handles the low-level tool injection (request_task_...) automatically.