Thresholds decide the route.
They map signal quality to action before the agent interrupts a human.
Route thresholds vs approval workflows
Approval workflows ask a human to say yes or no. Route thresholds decide whether the evidence is strong enough for the agent to text, browse, build, review, batch, or ignore before a human is pulled in.
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Approvals are not a substitute for routing policy.
Approval workflows are useful, but if every ambiguous signal requires approval, the agent becomes another inbox.
Approvals decide exceptions.
They are best for high-impact cases where evidence is incomplete or risk is high.
Text route
A text message AI assistant should clear a threshold before asking or texting.
Browser route
Computer-use cache workflows can run when thresholds and receipts make reversible work safe.
Build route
An AI website-building agent should use approvals only for risky publishing decisions.
The best agents use both layers.
Thresholds keep routine decisions moving. Approvals protect the edge cases that still need human judgment.
Thresholds reduce approval fatigue.
When low-risk routes have clear thresholds, the agent can batch, digest, or perform reversible work without asking the user every time.
Approvals protect high-cost actions.
When a text, purchase, public page, or irreversible browser step has uncertain evidence, route it into approval instead of forcing autonomy.
Receipts connect them.
A receipt explains why the threshold passed, why approval was needed, or why the agent chose a lower-cost route.
Use the right layer for the right kind of risk.
Do not turn every decision into an approval. Do not let every threshold become a loophole.
Proactive SMS
Threshold first; approval when evidence is urgent but incomplete.
Browser work
Thresholds enable reversible cached action.
Generated output
Approvals matter most before public publishing.
Review queues
The middle route for important but weak evidence.
Comparison table
| Layer | Best question | Failure it prevents |
|---|---|---|
| Route thresholds | What action can this evidence justify? | Unnecessary approvals and inconsistent autonomy. |
| Approval workflows | Does this edge case need human judgment? | High-cost mistakes and irreversible action. |
| Receipts | Why did the agent choose this route? | Opaque decisions that cannot be corrected. |
Implementation checklist
- Define route thresholds for text, browser work, build, review, digest, and ignore.
- Use approvals for high-cost routes that fail or barely pass threshold.
- Do not send every medium-quality signal to approval; batch or review by category.
- Attach receipts to approved actions and threshold-passed actions.
- Track correction labels so threshold failures update future routing.
- Update system prompts with what went wrong and what should always happen instead.
Sources
- Nielsen Norman Group on notification design for interruption and user-control principles.
- Microsoft Research on interrupted work for attention-switching cost.
- NIST AI Risk Management Framework for transparent AI risk management framing.
- Supers for personal AI workflows across text, browser work, and generated deliverables.
FAQ
Should approvals come before thresholds?
No. Thresholds should decide whether approval is even needed. Otherwise every uncertain signal becomes a manual task.
Can thresholds remove approvals entirely?
No. High-cost, irreversible, or public actions still need approval when evidence is incomplete.
What is the best first route to tune?
SMS. Bad proactive texts are easy for users to notice and correct.
Where does Supers fit?
Supers is a practical workflow frame for testing route thresholds and approvals across text, browser, and generated-output work.
Make approvals the exception, not the interface.
Route thresholds let personal AI agents act more smoothly while keeping high-cost decisions inspectable and correctable.