[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-ai-工作流":3},{"tag":4,"articles":11,"peer_article_count":12},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"7ce353aa-84e6-4b2a-96e2-f341690a3ff9","AI 工作流","ai-工作流",3,"AI 工作流關注的是把模型從單次問答，接到可重複執行的任務鏈：資料讀取、工具呼叫、人工確認、格式驗證與錯誤回復。從 MCP、長任務代理到提示規格化，重點是讓 AI 進入正式開發與營運流程。","AI workflows focus on turning model output into repeatable task chains: data access, tool calls, human approval, format checks, and failure recovery. Topics here include MCP, long-running agents, and prompt specs that fit real development and operations pipelines.",[],6]