Crypto Market Entry Strategy
This challenge focuses on building an advanced agentic system to devise a strategic market entry plan for a hypothetical crypto exchange. The system will employ a graph-based agent workflow using LangGraph's DAG patterns to orchestrate a team of expert agents. Key models will include Gemini 2.5 Pro (leveraging its Deep Think mode for complex financial and regulatory analysis) and Claude Sonnet 4 for competitive intelligence synthesis. The agents will perform RAG on vast datasets of market reports, regulatory documents, and competitor analyses, integrate with real-time market data tools, and engage in extended thinking cycles to refine strategic recommendations, including adaptive reasoning budgets for critical decision points.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a LangGraph-based agent workflow using Gemini 2.5 Pro Deep Think mode and Claude Sonnet 4 to generate a crypto market re-entry strategy with RAG and extended thinking.
How work is evaluated
Evaluation will assess the quality and coherence of the generated market entry strategy, the effectiveness of the LangGraph workflow in orchestrating agents, the depth of analysis provided by Gemini 2.5 Pro's Deep Think mode, and the utilization of RAG and tool integrations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design extended thinking cycles with adaptive reasoning budgets.
Build complex graph-based agent workflows using LangGraph DAG patterns.
Integrate real-time market data tools for dynamic insights.
Implement a RAG system for retrieving and synthesizing market data.
Orchestrate role-based expert agents for market intelligence and strategy.
Leverage Gemini 2.5 Pro Deep Think mode for sophisticated analysis.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Evaluation will assess the quality and coherence of the generated market re-entry strategy, the effectiveness of the LangGraph workflow in orchestrating agents, the depth of analysis provided by Gemini 2.5 Pro's Deep...
Challenge input
JSON: { "market_focus": "US", "duration_months": 6, "initial_capital": "$50M", "regulatory_updates": ["2024_SEC_guidance.pdf", "2024_CFTC_amendment...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON: { "strategy_document": "markdown_string", "langgraph_trace": "json_representation_of_workflow", "rag_summary": "string", "tool_calls_summary"...
- Verify that the LangGraph trace shows a complete and successful execution of the defined strategy workflow.
- Check if the 'rag_summary' indicates relevant information was retrieved and 'tool_calls_summary' shows inte...
- Strategic Soundness Score target: 8
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
No tool records are attached.
Evaluation contract
- Verify that the LangGraph trace shows a complete and successful execution of the defined strategy workflow.
- Check if the 'rag_summary' indicates relevant information was retrieved and 'tool_calls_summary' shows interaction with simulated market data/regulatory APIs.
Metrics
- Strategic Soundness Score · Target 8
- Deep Think Analysis Depth · Target 7
- Extended Thinking Evidence · Target 4
Workflow tasks
- Task 1
Input: JSON: { "market_focus": "US", "duration_months": 6, "initial_capital": "$50M", "regulatory_updates": ["2024_SEC_guidance.pdf", "2024_CFTC_amendments.pdf"], "competitor_analysis_reports": ["Binance_US_report_Q3_2024.pdf", "Coinbase_report_Q3_2024.pdf"], "simulated_market_data_api": "https://api.example.com/crypto_data" }
Output: JSON: { "strategy_document": "markdown_string", "langgraph_trace": "json_representation_of_workflow", "rag_summary": "string", "tool_calls_summary": "array_of_objects" }
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.