Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 14 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Outline the steps to prepare and deploy your Google ADK agent to Vertex AI. This includes creating a model, versioning, and deploying an endpoint. Once deployed, provide a simple Python script using the Vertex AI client libraries to interact with your agent, simulating user queries and demonstrating its personalized responses. Explain how multimodal input (text + simulated image data) would be sent to the deployed agent.
```python
# from google.cloud import aiplatform
# from google.cloud.aiplatform_v1.services import endpoint_service
# from google.cloud.aiplatform_v1.types import PredictRequest
# # Example for deployment (conceptual)
# # client_options = {"api_endpoint": "YOUR_REGION-aiplatform.googleapis.com"}
# # client = aiplatform.gapic.ModelServiceClient(client_options=client_options)
# # model = client.upload_model(...)
# # Example for interaction
# # endpoint = aiplatform.Endpoint(endpoint_name="your_endpoint_id")
# # response = endpoint.predict(instances=[{"user_id": "test_user", "query": "Hello"}])
# # print(response.predictions)
```Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the source structure until you know which part of the prompt is actually driving the result quality.
Change domain facts, examples, and tool context first before you rewrite the instruction scaffold.
Validate one failure mode at a time so prompt changes stay attributable instead of getting noisy.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Secure Personal Intelligence Agent with Google ADK & Gemini 2.5 Pro
Design and build a 'Personal Intelligence' agent using Google ADK and Gemini 2.5 Pro that securely accesses simulated personal data (e.g., from Gmail, Google Photos) to provide highly tailored and context-aware responses. This challenge focuses on creating a robust agent with strong emphasis on data privacy, secure access patterns, and explainability for personalized AI. The agent should demonstrate multimodal understanding by interpreting both text and image-based 'personal' data (e.g., a photo for context). The core of the challenge lies in managing sensitive user data within the Google ecosystem, employing best practices for data isolation and access control. You will implement a mechanism to simulate user consent and demonstrate how the agent would reason over private information while maintaining user trust and privacy. The solution should also include a developer interface for tracing and debugging the agent's reasoning process when handling personalized queries.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.