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Learn how to build resilient AI agent workflows that can pause, resume, and recover from failures using Agent Continuations, a programming concept adapted for AI systems. Discover how to capture an agent's complete execution state—including tools, goals, and partial responses—in a portable JSON format that enables unprecedented flexibility in agent management. Explore techniques for implementing human-in-the-loop approval gates, migrating agents across different platforms and cloud environments, and creating checkpoint systems for long-running multi-agent operations. Master the protocol specifications and examine practical Python implementations that work with standard OpenAI JSON APIs, making continuations compatible with existing agent frameworks. See live demonstrations of suspending multi-layer agent systems for human approval and learn how to transform monolithic agent architectures into restart-capable, human-aware services that reduce failure windows and enable new user experience patterns in AI applications.
