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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
GoogleADKAgentInitialization
Verify the Google ADK agent can be initialized with Gemini 2.5 Pro and tools.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
VertexAIDeploymentReadiness
Confirm the agent can be packaged and prepared for Vertex AI deployment.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
GraphiteClientConnection
Ensure a Graphite client can connect and log a simple event from the agent.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Contextual Relevance Score
Semantic similarity between generated response and ideal personalized response (0-1). • target: 0.88 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Privacy Policy Adherence Rate
Percentage of queries where data access strictly followed consent settings (0-1). • target: 1 • range: 0.9-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Multimodal Interpretation Accuracy
Accuracy of agent's understanding when combining text and image context (0-1). • target: 0.85 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Trace Completeness (Graphite)
Percentage of expected agent decisions and data calls present in Graphite traces (0-1). • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master Google ADK for building agents, including defining agent capabilities, tools, and memory management within the Google ecosystem.
Leverage Gemini 2.5 Pro's multimodal capabilities to process and reason over combined text and image data (simulated Gmail content, photo metadata).
Implement secure data access layers using Google Cloud KMS and Firestore (simulated) for storing and retrieving 'personal' information with fine-grained access control.
Design prompt engineering strategies for Gemini 2.5 Pro to generate contextually relevant and empathetic responses based on personal data while respecting privacy boundaries.
Integrate Graphite into the agent's workflow for real-time tracing, logging, and visualization of the agent's thought process and data access patterns.
Develop a user consent mechanism (simulated) that dictates the agent's scope of access to different types of personal data.
Deploy the Google ADK agent as a service on Vertex AI, configuring endpoints and ensuring secure API access.
Build a simple Streamlit or web interface to interact with the personalized agent and visualize Graphite traces.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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