What is human-in-the-loop AI?
Human-in-the-loop AI means the agent stops at chosen moments and asks a person to approve, edit, or reject the next action before taking it. You decide which actions need a check (sending an email, refunding a customer, posting publicly) and which the agent can run on its own.
Human-in-the-loop AI is the pattern where an agent stops at defined moments and waits for a person to approve, edit, or reject what it's about to do. The hole most builders hit: how do you let an agent draft customer emails or process refunds without it occasionally doing something embarrassing or expensive? Checkpoints answer that.
A few concrete shapes:
- An agent drafts an outbound email reply, then pauses for your approval in Slack before sending
- A sales agent qualifies inbound leads automatically, but routes any deal over $5,000 to a human before sending a quote
- A support agent triages tickets, but flags anything mentioning refunds, legal, or churn for human review
Platforms like Lindy, Gumloop, and Zapier Agents let you set these gates without code. In Gumloop, App Rules intercept a tool call and block or tag it based on conditions you define. LangChain calls the same idea an interrupt: the agent pauses, you approve, edit, reject, or respond, then it resumes.
The non-obvious bit about approval gates: they double as a dial for ramping supervision down as trust grows. Start with approval on every action. After watching the agent handle a category cleanly for a week, turn approval off for that action type and keep it on for riskier ones.
From a security standpoint
Checkpoints give you an audit trail for sensitive actions like outbound communications, payments, or writes to systems of record. Every approval is logged with who approved what and when.
Last updated: May 20, 2026