Should AI agents be allowed to execute their own decisions?
I've been building an AI-powered invoice and payment workflow, and one question keeps coming up:
Should an AI agent that recommends a payment also be allowed to execute it?
During testing, I ran into an interesting problem.
An invoice could look perfectly valid, but there was no confirmation that the work had actually been delivered.
The AI might still recommend paying it.
That pushed me toward a few design principles:
AI recommendations shouldn't automatically become actions.
Critical conditions should be checked by deterministic code, not just an LLM.
High-risk actions should require human approval.
Failed or blocked decisions deserve as much visibility as successful ones.
But there's a tradeoff.
Add too many safeguards, and automation becomes just another manual workflow.
Remove too many, and you're trusting an AI with actions that might be irreversible.
I'm curious how other makers are approaching this.
If you're building AI agents that can send emails, modify databases, move money, or take other real-world actions, how do you decide what requires human approval?
Would love to hear what's actually worked for you.
Replies
Yes, they can definitely execute actions if they are trained and built right. The core problem with current automated workflows is that the agents are often poorly trained for the job, which forces us to turn automation right back into a manual approval process.
Your second principle is the one we learned the hard way. A rule the model is only told to follow gets skipped now and then. A check in code doesn't.
Where we landed: the AI can prepare a payment and show why, but money only moves after a person says yes. And the way to keep safeguards from turning back into manual work is to stop only for things that can't be undone. Everything else just runs.