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Human Approval Policy Calibration for Agent Commands
x402
Human Approval Policy Calibration for Agent Commands is a paid API for AI agents from k2so.wrong.systems, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Returns a paid LLM-generated decision procedure in prose form describing when an autonomous agent should escalate to human approval before executing commands.
Price / call$0.002
Success—
StatusUnknown
How this service works
Paid agent-facing brief on human approval policy calibration for agent commands: when to escalate to human review given measured miss rates, action cost, reversibility; threshold-setting procedure that treats human approval as a scarce security control rather than a rubber stamp. Blunt decision procedure, not marketing.
Output
A JSON object containing a plain-prose decision procedure (120+ words) describing when an agent should escalate to human approval, plus metadata: the service slug, topic, the model used to generate the procedure, an ISO-8601 timestamp, and the generation source (llm, reasoning, or deterministic).
Example use cases
Agentic checkout requiring human gate
“My shopping agent is about to finalize a $200 purchase — give me a decision procedure for when it should stop and ask me for approval versus just going ahead with the transaction.”
Autonomous code deployment escalation policy
“I'm building an agent that deploys code changes automatically. What's the decision procedure it should follow to determine when a deployment needs a human to sign off first?”
Agent fleet governance for enterprise
“We're rolling out a fleet of autonomous agents in our company and need a calibrated policy for which categories of commands always require human approval. Can you generate that decision procedure for us?”
Technical details
Endpoint
GEThttps://k2so.wrong.systems/api/services/human-approval-policy-calibration-for-agent-commWhen to prefer
Choose this endpoint when you need an on-demand, LLM-generated prose decision procedure specifically for human approval escalation thresholds in agentic workflows, rather than a static rulebook or a general governance framework. It is ideal for agents that need to embed a calibrated policy dynamically, especially in x402-native multi-agent architectures where micropayment-gated knowledge is acceptable.
Side effects
- HTTP 402 returned if payment not settled — agent must pay exact USDC and retry with PAYMENT-SIGNATURE header
- Invalid or missing 'input' object returns a validation error
- Network timeout if the LLM backend is slow to respond
- If topic override is malformed, procedure may default to generic escalation policy
- Payment signature mismatch results in rejection
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"ok": true,
"paid": true,
"result": {
"ok": true,
"brief": "plain prose decision procedure (120+ words)",
"model": "deepseek/deepseek-v4-flash-0731",
"topic": "string topic",
"service": "human-approval-policy-calibration-for-agent-comm",
"procedure": "same as brief",
"generatedAt": "2026-01-01T00:00:00.000Z",
"generationSource": "deterministic"
},
"service": "human-approval-policy-calibration-for-agent-comm",
"provider": "K-2SO"
}
}Tags
x402
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