Guide2 min read
Human-in-the-Loop Design for AI Agents
The best agents are not fully autonomous on day one. They are designed to know when to ask, and they earn autonomy with measured performance.
TrueCodeAI Engineering
Agents, Voice & ML practice
- Published
AI agentsHuman-in-the-loopDesign
Where humans belong
| Action type | Example | Human role |
|---|---|---|
| Read and answer | Order status, policy question | Spot-check samples |
| Reversible write | Update CRM field, book slot | Review exceptions |
| Money or commitments | Refund, discount, quote | Approve above threshold |
| Irreversible or sensitive | Deletions, legal, medical | Always approve |
Hand-offs that work
- The agent passes a short summary, what it tried and why it is handing off.
- The customer is told a person is taking over and roughly when.
- The human’s decision is logged and becomes training for evals.
Approval UX
Approvals should take seconds: show the proposed action, the evidence, and approve/edit/reject buttons in the tool people already use — Slack, Teams, email or your admin panel. Slow approval flows get bypassed.
Earning autonomy
- Start with approval on all writes.
- Track approval rate per action type.
- When an action is approved unchanged the vast majority of the time over a meaningful sample, move it to spot-checks.
- Keep monitoring; tighten again if quality drops.
Frequently asked questions
Does human review defeat the point of automation?
No — reviewing a prepared action takes seconds, versus minutes to do the work.
Who does the reviewing?
The team that owned the task before — they know what correct looks like.
Can thresholds differ by customer?
Yes — approvals can depend on customer tier, amount or risk score.
