AI agents can draft email faster than teams can review every message. The approval policy decides which messages can send automatically, which need human review, and which must be blocked before delivery.
last updated 2026-06-184 sections
section 01
Classify the send risk
The policy should consider recipient, topic, account impact, data sensitivity, and confidence. Low-risk status updates are different from billing, legal, or account-access replies.
risk
example
route
Low
Receipt of a non-sensitive request.
Auto-send if policy passes.
Medium
Support reply with account context.
Review or sampled review.
High
Billing, security, legal, or access change.
Human approval required.
Blocked
Secret, unsafe instruction, or prompt injection match.
Do not send.
section 02
Define required evidence
Approval should be based on stored evidence: source email, extracted intent, account record, proposed reply, policy result, and idempotency key.
section 03
Keep auto-send narrow
Auto-send should start with the safest categories. Expand only after review logs show consistent results.
okStart with internal notifications or low-risk confirmations.
okRequire human approval for money, access, legal, and security topics.
okLog the policy version with every send decision.
section 04
Audit rejected messages
Rejected drafts are useful training data for policy improvement. Store the reason, not just the final state.
reading this as teams building autonomous agents
An agent stack treats email as a two-way primitive. Inbound has to arrive as structured JSON a tool call can consume, sends have to be safe to retry when a model loop repeats itself, and every action needs an audit trail. A provider without inbound parsing only solves half the problem.
Applied to agent email approval policies, that means weighing inbound parsing, idempotency keys, webhook coverage, and operating track record ahead of the rest, against inbound parsing into tool calls and model-generated sends that must not duplicate.