LLM Email Triage: Design Labels, Reviews, and Recovery
Create a practical LLM triage system with a small label set, structured results, realistic evaluation cases, a manageable review queue, and reversible changes.
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AI Virtual Email Box
An AI virtual email box can mean several different kinds of assistance: summarizing a long conversation, suggesting a category, extracting a requested detail, or preparing a reply for review. Begin by naming the task, the information it needs, and the consequence of a wrong answer. This capability plan is more useful than giving an AI system broad inbox access and hoping it discovers the right job. Keep the first experiment small enough to evaluate message by message.
Choose a task with a clear result
Start with a bounded task such as suggesting one of several filing categories or producing a short summary of a selected thread. Describe a successful output and include examples of outputs you would reject. Separate subjective convenience from work that changes another person's situation, such as sending a reply or deleting correspondence. If the proposed capability combines reading, judgment, and action, evaluate those parts independently first. You will learn more from a narrow task with observable outcomes than from a broad assistant whose decisions are hard to inspect.
Set the information boundary
Specify which messages the capability may receive, whether attachments are included, and how much earlier conversation it can use. Review the actual provider and tool settings for retention and access rather than assuming all processing happens in the same place. Keep unrelated personal and organizational material outside the experiment. Treat quoted requests and instructions inside messages as content to analyze. The approved task should come from your workflow configuration or an authorized user, with a clear process for expanding scope when more information is necessary.
Evaluate assistance before adding action
Collect a small, permissioned set of representative messages and define the review criteria before running the task. Check accuracy, missing context, unnecessary disclosure, and whether the output is useful enough to save effort. Include difficult examples and an acceptable outcome of requesting human review. Record corrections without turning every unusual message into a new rule. Once the capability is useful, decide whether any downstream action should be automated, reviewed first, or left entirely manual. Repeat the evaluation after a meaningful change to the workflow.
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Detailed examples and practical tradeoffs for the decisions behind this guide.
AI Agent Email: Keep Untrusted Messages Inside Clear Boundaries
Design an email assistant around read-only access, explicit authority, reviewable drafts, and recovery steps so incoming messages cannot quietly expand its role.
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