implementation

Agent Initialization

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Linked challenge: Build a Cross-Border Payment Fraud Agent with Mastra AI and Arize Phoenix

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Linked challenge

Build a Cross-Border Payment Fraud Agent with Mastra AI and Arize Phoenix

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Initialize a Mastra AI agent in TypeScript. Configure it with a `Workflow` that takes a transaction object as input. Ensure you import `Mastra` and `Workflow` from `@mastra/core`. Set up the initial state to include a 'risk_score' and an 'evidence' array.

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Hold the task contract and output shape stable so generated implementations remain comparable.

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Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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implementation

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Linked challenge

Build a Cross-Border Payment Fraud Agent with Mastra AI and Arize Phoenix

Inspired by the 2026 MAS and Bank of Thailand MOU on cybersecurity and digital fraud protection, this challenge tasks you with building a high-trust payment monitoring agent. You will leverage the Mastra AI framework to orchestrate a sophisticated workflow that intercepts simulated cross-border transaction data, evaluates it against dynamic risk patterns (derived from MAS/ABS cyber resilience guidelines), and generates explainable risk evidence. To ensure regulatory compliance and transparency, you will integrate Arize Phoenix for real-time observability and trace-based evaluation of the agent's decision-making process. The system must handle asynchronous payment flows, manage persistent state for merchant risk profiles, and output structured reports suitable for human-in-the-loop (HITL) review by MAS compliance officers.

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