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 Duration 21 hours

Course Outline

Foundations of X402 and Decentralized AI

  • Introduction to Coinbase’s X402 protocol
  • Motivation: Enhancing AI agent security via on-chain identity
  • System architecture and essential components

Establishing the Development Environment

  • Installing the X402 SDK and required dependencies
  • Setting up wallet and identity layers
  • Using Node.js and Python to create cross-language workflows

Exploring the X402 Protocol

  • Fundamentals of agent-wallet interactions
  • Processes for data signing, verification, and privacy
  • Patterns for secure communication and authorization

Embedding AI Models within X402 Applications

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

Crafting Smart Contracts for AI Engagement

  • Specifying agent permissions using Solidity
  • Processing LLM-initiated blockchain transactions
  • Debugging and testing decentralized AI behavior

Security, Regulatory Compliance, and Data Sovereignty

  • Navigating regulatory frameworks for AI and crypto
  • Ensuring data ownership and privacy-preserving computation
  • Auditing and protecting agent interactions

Advanced Architectures and Enterprise Adoption

  • Connecting X402 with corporate identity infrastructure
  • Engineering scalable, multi-agent systems
  • Case studies: AI-driven payments, analytics, and automation

Deployment and Operational Management

  • Executing decentralized AI agents in production environments
  • Monitoring and sustaining X402-based systems
  • Enhancing performance and reducing costs

Conclusion and Future Directions

Requirements

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

Target Audience

  • Software engineers building AI-integrated blockchain solutions
  • Enterprise architects investigating decentralized AI frameworks
  • Engineering leads developing secure, compliant AI agents leveraging on-chain infrastructure

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