ADK & A2A

• Key points are in the visual diagram above.

Understanding ADK & A2A helps you build reliable, scalable agent applications instead of fragile demos.

Think of ADK & A2A as a specialized capability in your Google ADK engineering toolkit.

Scenario

A production team in Google ADK uses ADK & A2A to handle a real user request — reducing manual work and improving response quality with proper validation and logging.

Solution

In Google ADK, apply ADK & A2A to this scenario: A production team in Google ADK uses ADK & A2A to handle a real user request — reducing manual work and improving response quality with proper validation and logging. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to ADK & A2A (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where ADK & A2A happens in the code.

1# ADK & A2A — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for ADK & A2A8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain adk & a2a clearly."},11        {"role": "user", "content": f"What is adk & a2a?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Skipping evaluation for ADK & A2A before production
• No logging or tracing around adk a2a steps
• Ignoring cost and latency implications

Quick recap — the most important points from this module.