· Updated 2026-10-16 · operations
A safe AI inbox triage workflow for support teams
AI email triage is easiest to control when it stops at a draft. A useful workflow reads only approved fields, labels the request, finds a supporting policy or help article, and prepares a reply for a person to check. It does not send, archive, or change a customer record on its own.
The example below is synthetic. Treat it as a design pattern for a bounded task, not as a claim that every inbox or integration is already connected.
1. Choose the smallest useful input
Start with a shared inbox and a defined request type. Decide which fields the task may receive, such as the message text, received time, language, and a random reference ID. Exclude attachments, full customer history, signatures, and personal details unless the task needs them and the business has approved that data path. Set who can access the task and how long its input and output are retained.
Write down what is out of scope: for example, legal threats, account access changes, payment disputes, safety reports, and requests that require a promise or exception. Those messages go to a named queue without an AI-written answer.
2. Return a small, reviewable result
Use a fixed output shape so an operator can scan each run:
- Category: one allowed label, such as billing, product help, account, sales, or other.
- Priority and reason: the urgency level plus the sentence that supports it.
- Source: the exact approved help article or policy section used. If none matches, return “no source found.”
- Draft: a short reply that answers only what the source supports.
- Review flag: ready for a person to check, or needs a specialist because information is missing, mixed, or sensitive.
Do not ask the model to invent a policy citation. The reference should resolve to a real article in the team’s approved knowledge base. Keep a human-readable explanation beside the label so an operator can reject a plausible-looking mistake.
3. Make the approval gate explicit
A person checks the recipient, category, evidence, and draft before sending. They can edit, approve, or reject it. The task should have no send permission by default; if a later integration adds sending, make that a separate, deliberately enabled action with its own confirmation and audit record. A draft is a suggestion, not a completed customer response.
For a synthetic normal case, imagine a customer asking where to find an invoice. The result might label it “billing,” cite the company’s actual invoice help page, and prepare: “You can download your invoice from Billing → Documents. Would you like us to point you to the right account?” The operator checks the link and account context before using it.
For an exception, imagine a message asking for a refund while the applicable policy is missing from the approved sources. The result should say “no source found,” leave the draft empty, and route the message to billing review. A confident tone is not evidence.
4. Define retry, stop, and success rules
- Retry: retry a transient tool or network failure once, with the same approved input. Do not retry to guess missing facts.
- Escalate: route unclear, multi-topic, sensitive, or unsupported requests to a person with the reason attached.
- Stop: give an operator a way to pause new runs, preserve the review queue, and resume only after checking the cause.
- Measure: review label corrections, drafts accepted with little editing, exception volume, response delay, and cost per run. Set thresholds from a representative trial; these are measures to collect, not claimed results.
Before a wider rollout, test ordinary requests, missing information, conflicting instructions, multiple questions, and attempts to make the system ignore its policy. Include a reviewer who can trace every draft to its source.
Where this fits
The AI Task Market services listing includes an inbox-triage task with per-run credits; check that page for the current listing and scope. For the human approval point, see how to place human review gates. For run records and safe retries, read the task observability guide. The pricing page explains the per-run credit unit.
A sensible first test is a small, approved sample with no send permission. Keep the task only if its labels and drafts save review effort without making the operator less able to see what happened.