Business Operations
Advanced
Always open

Build a Mineral Supply Chain Risk Agent

The US government is implementing a new strategy in Latin America to secure critical minerals like copper and rare earths, as evidenced by the $1.6B deal for USA Rare Earth and Ivanhoe's Chilean exploration. In this challenge, you will build an autonomous Supply Chain Intelligence Agent using the Mastra AI framework. The agent must orchestrate a RAG-based workflow that monitors mining news, identifies geopolitical risks in specific jurisdictions (e.g., Chile, Venezuela), and calculates a 'Supply Security Score' for specific commodities. You will integrate Arize AI to provide observability into the agent's decision-making process. Mastra AI's built-in memory will be used to track the evolution of mining M&A trends (like Zijin's $4B Allied Gold acquisition), while Arize AI will monitor for hallucinations or drift in the risk scoring logic. The final system should provide actionable alerts for supply chain managers when policy shifts or price surges (like Gold's recent warning signal) indicate impending volatility.

Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

The US government is implementing a new strategy in Latin America to secure critical minerals like copper and rare earths, as evidenced by the $1.6B deal for USA Rare Earth and Ivanhoe's Chilean exploration. In this challenge, you will build an autonomous Supply Chain Intelligence Agent using the Mastra AI framework. The agent must orchestrate a RAG-based workflow that monitors mining news, identifies geopolitical risks in specific jurisdictions (e.g., Chile, Venezuela), and calculates a 'Supply Security Score' for specific commodities. You will integrate Arize AI to provide observability into the agent's decision-making process. Mastra AI's built-in memory will be used to track the evolution of mining M&A trends (like Zijin's $4B Allied Gold acquisition), while Arize AI will monitor for hallucinations or drift in the risk scoring logic. The final system should provide actionable alerts for supply chain managers when policy shifts or price surges (like Gold's recent warning signal) indicate impending volatility.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Loading datasets...
Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.

Max Score: 3
Dimensions
3 scoring checks
Binary
3 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1mastra_agent_initialization

Mastra Agent Initialization

Checks if the Mastra agent starts correctly with its defined tools.

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2arize_connectivity

Arize Connectivity

Verifies that traces are being successfully sent to the Arize endpoint.

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 3risk_precision

Risk Precision

Accuracy of the risk score relative to human-labeled policy news. • target: 0.85 • range: 0-1

binary
Weight: 1
Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

Master the initialization of Mastra AI agents using TypeScript and the @mastra/core library

Implement persistent storage for agent memory to maintain context across multi-session mining trend analysis

Design custom Mastra Tools to interface with external news APIs and commodity price feeds

Integrate Arize AI Phoenix or SDK to capture traces of tool execution and evaluate prompt effectiveness

Build a RAG pipeline that indexes Latin American mining policy documents and M&A reports

Optimize agent workflows to handle high-volume commodity market signals like the 'Gold Surge' warning

Deploy the agent as a resilient service that provides automated risk reports via a REST API

Start from your terminal
$npx -y @versalist/cli start build-a-mineral-supply-chain-risk-agent

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

Docs
Manage API keys
Challenge at a glance
Host and timing
Vera

AI Research & Mentorship

Starts Available now
Evergreen challenge
Your progress

Participation status

You haven't started this challenge yet

Timeline and host

Operating window

Key dates and the organization behind this challenge.

Start date
Available now
Run mode
Evergreen challenge
Explore

Find another challenge

Jump to a random challenge when you want a fresh benchmark or a different problem space.

Useful when you want to pressure-test your workflow on a new dataset, new constraints, or a new evaluation rubric.

Tool Space Recipe

Draft
Evaluation
Rubric: 3 dimensions
·Mastra Agent Initialization(1%)
·Arize Connectivity(1%)
·Risk Precision(1%)
Gold items: 1 (1 public)

Frequently Asked Questions about Build a Mineral Supply Chain Risk Agent