Multi-turn email conversations with an AI agent

Building an AI agent capable of multi-turn email conversations requires a robust approach to managing conversation state across restarts and delays. The process involves creating a durable record for each conversation, keyed to a unique thread ID provided by Nylas, ensuring the agent can restore context even after periods of inactivity. The agent must handle incoming replies through webhooks, filtering out its own messages, and determining the next step in the conversation based on pre-defined steps like "awaiting_reply" or "escalated." Using a language model (LLM), the agent generates contextually appropriate replies by accessing the full conversation history, ensuring a coherent interaction flow. The system must also manage lifecycle events, such as conversation completion or escalation due to prolonged inactivity or complexity, and requires careful consideration of production challenges like message deduplication and thread locking to avoid duplicate or conflicting responses.

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