feat(typescript): introduce standalone sample

[miguelvelasquezdev]

Implements a standalone TypeScript sample with Genkit and @a2a-js/sdk.

Includes ShoppingAgent, CredentialsProviderAgent, MerchantAgent, and MerchantPaymentProcessorAgent with full payment workflow support.

Description

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Closes #67 🦕

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Summary of Changes

Hello @miguelvelasquezdev, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request delivers a new, self-contained TypeScript sample that showcases a complete agent-to-agent payment flow. It integrates multiple AI agents, each responsible for a specific part of the commerce transaction, from a shopper's initial product search to the final payment processing, demonstrating how these agents collaborate to fulfill a user's request.

Highlights

• New TypeScript Sample: Introduces a comprehensive standalone TypeScript sample demonstrating an end-to-end payment workflow using Genkit and the @a2a-js/sdk.
• Agent Implementation: Includes implementations for a ShoppingAgent, CredentialsProviderAgent, MerchantAgent, and MerchantPaymentProcessorAgent, all working together to simulate a full payment process.
• Payment Workflow Support: The sample provides full support for a payment workflow, covering aspects from product search and cart management to shipping address collection, payment method selection, and payment processing, including handling OTP challenges.
• CLI for Interaction: A command-line interface (CLI) is included to easily interact with the various agents and observe the workflow.

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Code Review

This pull request introduces a comprehensive standalone TypeScript sample demonstrating the capabilities of Genkit and the @a2a-js/sdk for an e-commerce payment workflow. However, it contains critical security vulnerabilities, primarily related to Broken Access Control (IDOR) and LLM Safety (Prompt Injection), which could allow an attacker to steal sensitive user information. It is highly recommended to address these by implementing proper authentication/authorization and following LLM safety best practices. Additionally, several issues were identified, particularly with dependency versions in package.json, and suggestions for code improvements related to type safety, robustness, and best practices have been provided.