AI agents become dangerous when they get more rights than the process can handle.
The solution isn't to stop everything. The solution is to put approval in the right places.
For a Swedish SMB, that often means a simple principle: the agent gets to prepare more than it gets to execute.
What is an approval loop?
An approval loop is a checkpoint where the agent must ask a human to approve the next step.
Examples:
- The agent writes a LinkedIn post, but Max approves before publishing.
- The agent summarizes a customer case, but the support lead sends the reply.
- The agent suggests a CRM update, but the sales rep confirms before fields change.
- The agent creates a blog draft, but no publication happens without CMS approval.
It sounds simple. It's often the difference between a usable agent workflow and a risky demo.
Put approval where the consequence is
Not all steps need approval.
Low-risk steps:
- read data
- classify incoming cases
- summarize information
- create drafts
- suggest next steps
High-risk steps:
- send external messages
- publish content
- change customer data
- create invoices or contracts
- make decisions that affect price, legal, or customer relationships
Put approval before high-risk steps. Let the agent work faster before that point.
A simple model for small businesses
Use four levels:
Level 1: Draft
The agent creates proposals. A human handles everything external.
Works for content, email drafts, and internal analysis.
Level 2: Prepare
The agent fills systems or documents, but saves as draft.
Works for CRM, quotes, and support.
Level 3: Execute with approval
The agent performs the action after approval.
Works when the workflow is stable and logs exist.
Level 4: Execute within limits
The agent performs low-risk actions on its own within clear boundaries.
Works only when you have history, measurement, and rollback.
The log is part of the product
An agent workflow without a log becomes hard to trust.
Log at minimum:
- input the agent read
- tools the agent used
- suggestions the agent created
- who approved
- time of approval
- what was published or changed
That log helps you improve prompts, find error sources, and show why a decision was made.
Governance without enterprise theater
Small businesses don't need to start with a big AI governance program.
Start with five questions:
- Who owns the workflow?
- Which systems can the agent read?
- Which systems can the agent write to?
- Where is human approval required?
- How do we measure errors, time, and value?
Answer those and you can build safer than many larger companies.
Next step
Pick one agent workflow and mark each step as read, suggest, prepare, approve, or execute. Build the first version with more checkpoints than you think you need. Remove them only when data shows the workflow holds.