planning

Design Google ADK Agent for Personal Intelligence

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Linked challenge: Secure Personal Intelligence Agent with Google ADK & Gemini 2.5 Pro

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Linked challenge

Secure Personal Intelligence Agent with Google ADK & Gemini 2.5 Pro

Prompt source

Original prompt text with formatting preserved for inspection.

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Outline the architecture for a Google ADK agent that acts as a 'Personal Intelligence' assistant. Define the specific tools the agent would need (e.g., simulated 'Gmail Reader', 'Photo Analyzer'). Describe how agent memory will maintain user context and how 'privacy consent levels' will be managed to restrict tool access. Detail how Gemini 2.5 Pro will be used for multimodal input processing and response generation. Emphasize security and explainability.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.

Tune next

Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.

Verify after

Check whether the prompt asks for the right evidence, confidence signal, and escalation path.

Prompt diagnostics

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Purpose

planning

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

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.

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