Hyperscale Data Center Investment
Develop an advanced multi-agent system using CrewAI to analyze the hyperscale data center market. The system should leverage cutting-edge generative AI models like GPT-5 for deep market trend identification, competitive analysis, and strategic investment opportunity generation. Agents will perform extensive research, synthesize complex information from various sources (news, financial reports, industry analyses), and utilize MCP-enabled tools for real-time data integration with enterprise financial systems. This challenge emphasizes building robust, collaborative agent workflows capable of extended thinking to provide actionable insights for investment decisions. Participants will focus on designing clear agent roles, orchestrating their interactions, and integrating external data sources to generate comprehensive market intelligence reports, including due diligence summaries for identified opportunities.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a multi-agent system using GPT-5 for deep market analysis and investment opportunity identification in the data center sector.
How work is evaluated
The system will be evaluated on its ability to accurately identify market trends, pinpoint relevant investment opportunities, and provide a concise due diligence summary, demonstrating effective agent collaboration and tool integration.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design and implement a robust multi-agent system using CrewAI for market intelligence.
Generate comprehensive market trend reports and actionable investment opportunity summaries.
Utilize MCP-enabled tool integration for accessing real-time financial and market data.
Implement extended thinking patterns for deep contextual analysis and strategic recommendations.
Integrate GPT-5 for advanced analytical reasoning and complex information synthesis.
Reference links and supporting material
Sample data for 3 tasks
Challenge setup
The system will be evaluated on its ability to accurately identify market trends, pinpoint relevant investment opportunities, and provide a concise due diligence summary, demonstrating effective agent collaboration an...
Challenge input
{"market_reports": ["URL1", "URL2"], "news_articles": ["URL3"]}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{"key_trends": ["trend1", "trend2"], "market_drivers": ["driver1"], "competitive_landscape_summary": "summary"}
- Ensure all output JSONs strictly adhere to the defined schemas.
- Verify successful handoff and communication between CrewAI agents based on task completion and log analysis.
- Confirm evidence of MCP-enabled tool calls (e.g., financial API calls) in the agent's reasoning traces or o...
- MarketInsightAccuracy target: 90
- 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.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Ensure all output JSONs strictly adhere to the defined schemas.
- Verify successful handoff and communication between CrewAI agents based on task completion and log analysis.
- Confirm evidence of MCP-enabled tool calls (e.g., financial API calls) in the agent's reasoning traces or output.
Metrics
- MarketInsightAccuracy · Target 90
- OpportunityRelevanceScore · Target 4
- DueDiligenceCompleteness · Target 85
- ReasoningDepthScore · Target 4
Workflow tasks
- Task 1
Input: {"market_reports": ["URL1", "URL2"], "news_articles": ["URL3"]}
Output: {"key_trends": ["trend1", "trend2"], "market_drivers": ["driver1"], "competitive_landscape_summary": "summary"}
Execution harness: Python
- Task 2
Input: {"market_trends_analysis": {"key_trends": [...], "market_drivers": [...]}, "mcp_tool_data": {"financial_indicators": {...}}}
Output: {"opportunities": [{"name": "Op1", "type": "Greenfield", "location": "Indonesia", "rationale": "High demand, low supply, strong government support"}], "risks": ["risk1", "risk2"]}
Execution harness: Python
- Task 3
Input: {"opportunity_details": {"name": "...", "location": "...", "rationale": "..."}, "mcp_tool_results": {"financial_data": {...}, "regulatory_status": {...}}}
Output: {"due_diligence_report": {"executive_summary": "...", "financial_analysis": "...", "risk_assessment": "...", "recommendation": "..."}}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.