办公

Outlook 邮件分拣 AI Agent 工作流

面向 Microsoft 365 的长尾工作流：分类 Outlook 邮件、起草回复、创建后续任务并升级高风险邮件。

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办公工作流

Outlook 邮件分拣 AI Agent 工作流

依赖 Outlook 处理客户、财务、支持和管理层沟通的 Microsoft 365 团队。

办公Outlook邮件Microsoft 365收件箱分拣Graph API效率AI Agents
适合场景

依赖 Outlook 处理客户、财务、支持和管理层沟通的 Microsoft 365 团队。

页面类型

办公

属性

Outlook / 邮件分拣 / Microsoft 365

如何使用

把这个工作流作为起点，再根据自己的流程调整工具、提示词和复核步骤。

概览

Outlook 邮件分拣 AI Agent 工作流比普通收件箱自动化更窄，适合已经使用 Microsoft 365 的团队，用于分类、标旗、起草回复和生成后续任务。

Microsoft Graph 提供发件人、附件、重要性、flag、已读状态和文件夹上下文等邮件字段，可以支撑 Agent 排序和准备动作，同时保留用户对发送和删除的控制。

低风险设计应先做助手：分类、总结、起草和建议任务。法律、财务、HR、安全或管理层决策相关邮件必须升级人工复核。

使用场景

  • 创始人每天收到 Outlook 紧急线程列表和简短回复草稿。
  • 支持经理区分客户投诉、续费、发票和会议请求。
  • 运营团队把供应商邮件转成带截止日期的后续任务。
  • 行政助理标记重点发件人的邮件并准备可复核回复。

实施步骤

  1. 定义分拣类别. 先用少量类别，例如紧急客户、财务、内部审批、会议请求、新闻邮件、供应商和无需行动。
  2. 限制 Graph 权限. 只读取分拣需要的文件夹和字段，在规则稳定前不要开放过宽邮箱动作。
  3. 生成行动建议. 为每封邮件输出优先级、理由、建议分类、回复草稿、截止日期和责任人。
  4. 发送动作保留人工复核. Agent 可以起草回复，但发送、删除、转发敏感内容和破坏性动作都应审批。
  5. 创建后续任务. 把真实承诺转成 Planner、To Do 或 CRM 任务，并带上来源邮件链接。
  6. 审计分拣决定. 记录分类调整、忽略建议、编辑草稿和升级动作，用来持续改善规则。

配置步骤

  1. 先从一个文件夹或共享邮箱开始，不要直接覆盖整个账号。
  2. 把 Outlook 分类映射到明确业务动作。
  3. 为 HR、法律、密码重置、健康和私人邮件设置排除规则。
  4. 为 VIP 发件人设置白名单，为低价值自动化设置屏蔽规则。
  5. 每周复核误判样本并更新分类提示词。

快速判断

主要意图给 Outlook 邮件排序并准备下一步动作。
第一步自动化分类和起草，发送仍由人工确认。
关键 Microsoft 字段发件人、重要性、附件、flag、已读状态和会话 ID。
成功指标响应更快，重点邮件漏处理更少。

常见问题

Outlook Agent 应该自己发送回复吗？

先从草稿和审批开始。自动发送只适合范围很窄、低风险且已经有复核数据证明可靠的模板。

它和 Gmail 分拣有什么不同？

主要区别在 Microsoft 365 环境：Outlook 分类、Microsoft Graph 邮件字段、Planner、To Do、Teams 和企业权限边界。

哪些邮件必须升级人工？

法律、HR、财务、安全、管理层、合同、付款和个人数据相关邮件都应交给人工负责人。

相关资源

Gmail AI 邮件分拣工作流 面向 Gmail 的邮件分拣工作流，把未读邮件整理成优先级、回复草稿、后续任务和可审核的日常决策。 AI 从 Slack 和邮件生成项目状态报告 把 Slack 更新和邮件线程整理成项目状态报告，包含进度、决策、阻塞、风险和下一步的办公工作流。 AI Teams 会议记录转 Planner 任务工作流 把 Microsoft Teams 会议记录、转录和聊天后续事项转换成带负责人、日期和来源链接的 Planner 任务。 AI 会后跟进邮件工作流 起草会后跟进邮件的实用工作流，包含决策、下一步、责任人、截止日期和符合关系的语气。 AI 发票邮件转表格工作流 面向长尾搜索的办公工作流：从 Gmail 或 Outlook 发票邮件提取字段，校验金额，并写入 Google Sheets 或 Excel。
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Workflow Directory

Browse curated agents, MCP servers, templates, and workflow examples for real AI automation projects.

AI agent stack research

Find the right AI agents, MCP servers, and workflow templates

Agent Stack Library is a practical directory for people who are building real AI automation systems, not just collecting tool names. The site brings together AI agent frameworks, MCP servers, workflow templates, coding agents, browser automation tools, research workflows, and SaaS operations playbooks so you can compare an entire agent stack before committing to a toolchain.

A useful AI agent stack usually needs more than one model or one chat interface. Teams need a clear workflow, safe tool permissions, repeatable prompts, review checkpoints, and a way to measure whether the output is good enough for production. That is why the directory focuses on use cases such as AI coding agents, MCP server selection, SEO content workflows, browser QA, research assistants, internal tools, and multi-agent orchestration.

If you are evaluating MCP servers for AI agents, start with the task. A coding agent often needs GitHub access, a narrow filesystem scope, a test runner, and browser or DevTools verification. A research agent may need web search, document parsing, citation capture, memory, and a review step. A business operations agent may need CRM, email, calendar, spreadsheet, and audit logs. The best stack is the smallest one that completes the job safely.

AI agent workflow templates

Workflow templates help turn one-off prompts into repeatable systems. Each template should define the trigger, input context, agent role, connected tools, output format, human review step, and success metric. Browse the AI Agent Workflow Templates guide for SEO, coding, research, browser automation, and SaaS operations examples.

MCP servers for AI agents

MCP servers connect agents to browsers, repositories, files, databases, memory, and business apps. Good MCP choices reduce custom integration work, but they also require clear permission boundaries. The Best MCP Servers for AI Agents guide explains how to pick a safe and useful tool stack.

AI coding agent workflow

Coding agents work best when they follow a normal engineering path: issue intake, repo context, plan, patch, tests, UI verification, pull request, and human review. The AI Coding Agent Workflow page gives a practical checklist for scoped code changes.

How to choose an agent stack

Start by deciding what the agent is allowed to do. Read-only workflows are easier to launch because the agent can gather context, summarize findings, and draft recommendations without touching production systems. Write-capable workflows need stricter guardrails: scoped credentials, test environments, logging, rollback procedures, and a human approval point before external actions.

Next, compare tools by workflow fit rather than popularity. An open-source agent framework may be perfect for a developer team that wants full control, while a managed automation platform may be better for operations teams that need quick integrations. A browser automation stack is useful for UI checks and web research, but it should not replace structured APIs when reliable APIs exist.

Finally, measure quality. Track task completion rate, review time, correction rate, cost per run, latency, and whether the output can be reused without heavy manual cleanup. A strong AI agent workflow is not the one with the most tools; it is the one that produces reliable output, exposes failures clearly, and lets humans stay in control where the risk is high.

What each directory category is for

The Agents category covers frameworks, SDKs, and agent products that help teams plan, call tools, manage memory, hand off work, or coordinate multiple specialist agents. Use this category when you are comparing LangGraph-style orchestration, coding agents, research agents, customer support agents, or open-source agent frameworks for a production project.

The MCP Tools category is focused on servers and integrations that let an AI agent interact with the outside world. These pages are useful when you need repository context, browser inspection, file access, databases, calendars, CRMs, or other business systems. Each MCP server should be judged by permission scope, reliability, setup effort, documentation quality, and how clearly failed tool calls are reported.

The Workflows and Templates categories are for readers who already know the job they want to automate. Instead of starting with a tool, start with a repeatable process: SEO content briefing, GitHub issue triage, browser QA, competitive research, sales lead enrichment, or support ticket summarization. From there, pick the smallest agent stack that can collect the right context, run the task, produce a reviewable output, and leave a log for future improvement.