Orchestrate a Geopolitical Supply Chain Risk Assessment Crew
Headlines underscore the fragility of global supply chains to geopolitical events. This challenge focuses on building a sophisticated multi-agent system using CrewAI to analyze such complex situations. Your CrewAI-orchestrated team will consist of specialized agents (e.g., Geopolitical Analyst, Supply Chain Modeler, Risk Mitigation Strategist) powered by Claude Opus 4.1 for deep strategic analysis and Claude Sonnet 4 for rapid information gathering. The agents will communicate securely using an A2A protocol for cross-platform information exchange. The system will integrate RAG over geopolitical databases and international trade agreements, and adapt its thinking budget based on the criticality of the detected risk to provide timely and comprehensive risk assessments and mitigation strategies.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate a CrewAI multi-agent system with Claude Opus 4.1, A2A protocol, and adaptive thinking for geopolitical supply chain risk assessment.
How work is evaluated
The evaluation will assess the CrewAI system's effectiveness in accurately identifying geopolitical supply chain risks, generating actionable insights, and proposing well-reasoned mitigation strategies through collaborative agent work.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design hybrid reasoning for nuanced geopolitical and supply chain insights.
Implement secure agent-to-agent communication using A2A protocol.
Generate comprehensive risk assessments and mitigation plans.
Integrate RAG for accessing specialized legal and policy documents.
Orchestrate a role-based multi-agent team for complex analysis.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the CrewAI system's effectiveness in accurately identifying geopolitical supply chain risks, generating actionable insights, and proposing well-reasoned mitigation strategies through collabo...
Challenge input
{'event_description': 'China cut off Nexperia\'s outbound shipments due to Netherlands\' seizure.', 'target_supply_chain': 'European automotive sem...
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'event_summary': '...', 'direct_impacts': ['...'], 'indirect_impacts': ['...'], 'involved_actors': ['...'], 'criticality_score': 1-5, 'explanation...
- Verify the CrewAI agents successfully complete their assigned tasks and collaborate as defined.
- Confirm that A2A protocol is utilized for secure data exchange between agents (check logs or simulated comm...
- Check if RAG-enabled tools were invoked to retrieve relevant geopolitical or trade agreement data.
- Risk Assessment Accuracy target: 0.9
- 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
- Verify the CrewAI agents successfully complete their assigned tasks and collaborate as defined.
- Confirm that A2A protocol is utilized for secure data exchange between agents (check logs or simulated communication channels).
- Check if RAG-enabled tools were invoked to retrieve relevant geopolitical or trade agreement data.
Metrics
- Risk_Assessment_Accuracy · Target 0.9
- Mitigation_Strategy_Quality · Target 4
- Agent_Collaboration_Efficiency · Target 4
Workflow tasks
- Task 1
Input: {'event_description': 'China cut off Nexperia\'s outbound shipments due to Netherlands\' seizure.', 'target_supply_chain': 'European automotive semiconductor supply chain', 'country_focus': 'Netherlands, China, Germany'}
Output: {'event_summary': '...', 'direct_impacts': ['...'], 'indirect_impacts': ['...'], 'involved_actors': ['...'], 'criticality_score': 1-5, 'explanation': '...'}
Execution harness: Python
- Task 2
Input: {'risk_analysis': {'event_summary': '...', 'criticality_score': 4, 'direct_impacts': ['...'], 'involved_actors': ['...']}, 'stakeholder_priorities': ['cost-efficiency', 'resilience']}
Output: {'mitigation_strategies': [{'strategy_title': '...', 'description': '...', 'feasibility': 'high/medium/low', 'estimated_impact': 'high/medium/low'}], 'overall_recommendation': '...'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.