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 Duration 21 hours

Course Outline

Introduction to X402 and Decentralized AI

  • Overview of Coinbase’s X402 protocol
  • Rationale: Establishing secure AI agents with on-chain identity
  • System architecture and core components

Preparing the Development Environment

  • Installation of the X402 SDK and required dependencies
  • Configuration of wallets and identity layers
  • Incorporating Node.js and Python for cross-language workflows

Exploring the X402 Protocol

  • Fundamental principles of agent-wallet interactions
  • Data signing, verification processes, and privacy mechanisms
  • Patterns for secure communication and authorization

Incorporating AI Models into X402 Applications

  • Linking OpenAI, DeepSeek, Qwen, and Mistral Small
  • Overseeing model inference and token consumption
  • Creating autonomous, wallet-aware AI agents

Building Smart Contracts for AI Engagement

  • Defining agent permissions in Solidity
  • Processing LLM-driven blockchain transactions
  • Testing and troubleshooting decentralized AI operations

Security, Compliance, and Data Sovereignty

  • Regulatory aspects regarding AI and cryptocurrency
  • Data ownership and privacy-preserving computational methods
  • Auditing and securing agent interactions

Advanced Architectures and Enterprise Integration

  • Integrating X402 with corporate identity platforms
  • Designing scalable, multi-agent infrastructures
  • Case studies: AI-driven payments, analytics, and automation

Deployment and Operational Management

  • Running decentralized AI agents in production environments
  • Monitoring and maintaining X402-based systems
  • Optimizing performance and cost efficiency

Requirements

  • Familiarity with fundamental blockchain concepts
  • Practical experience in API integration and smart contract creation
  • Foundational knowledge of large language models and prompt engineering

Target Audience

  • Software engineers developing blockchain applications with AI integration
  • Enterprise architects investigating decentralized AI frameworks
  • Engineering leads designing secure, compliant AI agents utilizing on-chain systems

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