Global AI Chip Supply Chain Resilience
Address the critical challenge of maintaining a resilient global AI chip supply chain. This challenge involves building a multi-agent system that simulates key nodes in a supply chain, monitors geopolitical and market events, and dynamically proposes strategies to mitigate disruptions. The system will utilize Gemini 2.5 Pro, particularly its 'Deep Think' mode, for advanced analytical reasoning and scenario planning to predict potential bottlenecks and identify alternative sourcing. AutoGen will be employed to orchestrate a decentralized network of 'Supply Chain Node' agents (e.g., Manufacturer Agent, Logistics Agent, Procurement Agent), each responsible for a part of the simulated supply chain. These agents will use hybrid reasoning (instant assessment of news, deep analysis of impact) and integrate with simulated ERP and logistics APIs to track inventory, production, and shipments, adapting their plans in response to real-time RAG-fed geopolitical and economic data.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a multi-agent system with AutoGen and Gemini 2.5 Pro (Deep Think) to simulate and optimize global AI chip supply chain resilience against disruptions.
How work is evaluated
The evaluation will assess the system's ability to identify, analyze, and mitigate disruptions in a simulated AI chip supply chain, focusing on the quality of proposed solutions and the efficiency of adaptation.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Implement RAG to feed agents real-time geopolitical and market intelligence.
Generate adaptive supply chain strategies to mitigate simulated disruptions.
Develop tools for agents to interact with simulated ERP/logistics systems.
Orchestrate a multi-agent simulation of an AI chip supply chain using AutoGen.
Integrate Gemini 2.5 Pro (Deep Think) for complex analytical reasoning and risk assessment.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The evaluation will assess the system's ability to identify, analyze, and mitigate disruptions in a simulated AI chip supply chain, focusing on the quality of proposed solutions and the efficiency of adaptation.
Challenge input
JSON object with 'initial_supply_chain_state': object, 'disruption_event': { 'type': string, 'location': string, 'impact': string, 'details': string }
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON object with 'impact_assessment': string, 'mitigation_plan': { 'actions': string[], 'affected_nodes': string[], 'estimated_recovery_time': stri...
- Verify active participation and communication among defined AutoGen agents during simulation.
- Ensure simulated ERP/logistics tools are correctly invoked and their outputs processed by agents.
- Mitigation Strategy Effectiveness target: 4
- 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 active participation and communication among defined AutoGen agents during simulation.
- Ensure simulated ERP/logistics tools are correctly invoked and their outputs processed by agents.
Metrics
- Mitigation Strategy Effectiveness · Target 4
- Disruption Impact Accuracy · Target 4
- Adaptation Speed · Target 60
Workflow tasks
- Task 1
Input: JSON object with 'initial_supply_chain_state': object, 'disruption_event': { 'type': string, 'location': string, 'impact': string, 'details': string }
Output: JSON object with 'impact_assessment': string, 'mitigation_plan': { 'actions': string[], 'affected_nodes': string[], 'estimated_recovery_time': string }, 'reasoning_trace': string
Execution harness: Python
- Task 2
Input: JSON object with 'demand_forecast': object, 'supplier_data': object, 'geopolitical_updates': string
Output: JSON object with 'sourcing_recommendation': object, 'justification': string
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.