Agent for Real Time Public Policy Analysis
This challenge focuses on building an intelligent agent that can perform real-time policy impact analysis. Participants will leverage Gemini 3 Pro for its advanced reasoning capabilities and DSPy for programmatic prompt optimization, ensuring robust and structured output. The agent will employ a hybrid reasoning approach to distinguish between instant interpretations and deep, contextualized analysis. A crucial component will be an MCP-enabled RAG system, integrating with external legal databases and real-time regulatory news feeds to provide the most accurate and up-to-date policy context, translating abstract rules into concrete business implications.
AI Research & Mentorship
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on building an intelligent agent that can perform real-time policy impact analysis. Participants will leverage Gemini 3 Pro for its advanced reasoning capabilities and DSPy for programmatic prompt optimization, ensuring robust and structured output. The agent will employ a hybrid reasoning approach to distinguish between instant interpretations and deep, contextualized analysis. A crucial component will be an MCP-enabled RAG system, integrating with external legal databases and real-time regulatory news feeds to provide the most accurate and up-to-date policy context, translating abstract rules into concrete business implications.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master DSPy for programmatically structuring prompts, defining signatures, and optimizing LLM calls with Gemini 3 Pro for consistent and high-quality policy interpretations.
Implement a hybrid reasoning system that intelligently switches between 'instant' (quick summary) and 'deep thought' (detailed legal analysis) modes based on the complexity of policy clauses or specific queries.
Build a robust MCP-enabled RAG pipeline for fetching and contextualizing relevant legal precedents, prior Executive Orders, and AI policy discussions from a simulated vector database of regulatory texts.
Design MCP-enabled tool integration to query external legal databases (e.g., LexisNexis API simulator) and monitor real-time news feeds for rapid updates on regulatory changes.
Develop a structured output generation module using DSPy's `TypedPredictor` or similar mechanisms for consistent impact reports (e.g., JSON schema for risk assessment, affected sectors, compliance challenges).
Evaluate agent performance using DSPy's built-in metrics and optimization capabilities, fine-tuning prompt designs based on human-annotated policy interpretation examples.
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Operating window
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