Lab 19: Building a Collaborative Travel Team

Goal

In this lab, you will build a Travel Planning Team consisting of a Coordinator, a Weather Specialist, and a Flight Booker. You will learn how to use the mode parameter to create a system where sub-agents fulfill specific tasks and then automatically return control to the main planner.

The Scenario

• Coordinator receives the user's travel request.
• It delegates to the Weather Specialist (in single_turn mode) to get a quick forecast.
• It then delegates to the Flight Booker (in task mode) to find and "book" a flight (allowing for some back-and-forth about preferences).
• Finally, the Coordinator synthesizes the plan.

Step 1: Create the Project

uv run adk create travel_team
cd travel_team

Step 2: Implement the Collaborative Nodes

Open agent.py. Your task is to define the team and configure their modes correctly.

Exercise: Complete the agent definitions by setting the appropriate mode for each specialist.

# In agent.py (Starter Code)
from google.adk import Agent

# Note: every agent below needs rerun_on_resume=True. Any node that's part
# of a sub_agents dispatch chain can be "woken up" when a task-mode
# sub-agent pauses and resumes across turns -- without the flag on ALL
# three agents (coordinator included), you'll hit:
# "ValueError: A node must have rerun_on_resume=True."

# 1. Define the Weather Specialist
# Hint: Use 'single_turn' mode for a quick, non-interactive lookup.
weather_agent = Agent(
    name="weather_checker",
    model="gemini-3.5-flash",
    rerun_on_resume=True,
    instruction="""
    # TODO: Write instructions to provide a brief 3-day forecast 
    # for the requested destination.
    """
)

# 2. Define the Flight Booker
# Hint: Use 'task' mode to allow the agent to ask the user 
# questions about their flight preferences before finishing.
flight_agent = Agent(
    name="flight_booker",
    model="gemini-3.5-flash",
    rerun_on_resume=True,
    instruction="""
    # TODO: Write instructions to help the user book a flight. 
    # Ask about preferred airline or time if not provided.
    """
)

# 3. Define the Coordinator
# Hint: Coordinator should NOT have a mode set (it's the root), but it
# still needs rerun_on_resume=True (see note above).
root_agent = Agent(
    name="travel_planner",
    model="gemini-3.5-flash",
    rerun_on_resume=True,
    instruction="""
    # TODO: Write instructions to coordinate the team.
    # 1. Get weather from weather_checker.
    # 2. Book flight via flight_booker.
    # 3. Present the final cohesive plan.
    """,
    # TODO: Register your specialists here
    sub_agents=[] 
)

Step 3: Run and Test

1. Launch the Dev UI:

   uv run adk web .

2. Verify the Flow:
   • Ask: "I want to go to Tokyo next week."
   • Observe: The travel_planner should call the weather_checker.
   • Observe: Then it should call the flight_booker. The flight booker might ask you "Which airline do you prefer?" or "Do you want a morning flight?".
   • Final Check: After you answer, notice how control automatically returns to the travel_planner without any "hand-off" code.

Lab Summary

You have built a Collaborative Agent Team!

• You used mode="single_turn" for background utility tasks.
• You used mode="task" for interactive sub-tasks with automatic return.
• You observed how the ADK 2.0 framework manages the hand-off lifecycle so you can focus on agent logic.

Self-Reflection Questions

• Why would you use single_turn instead of task for a database lookup node?
• What happens to the conversation history when a sub-agent is in task mode? (Hint: Check the Trace tab).
• How does the mode setting improve the reliability of complex, multi-step workflows compared to standard chat mode?

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🕵️ Hidden Solution 🕵️

Looking for the solution? Here's a hint (Base64 decode me): L2RvYy1hZGstdHJhaW5pbmcvbW9kdWxlMTktY29sbGFib3JhdGl2ZS10ZWFtcy9sYWItc29sdXRpb24=

The direct link is: Lab Solution