{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "name": "Agentic Purchase Quality Verification",
  "acronym": "APQV",
  "version": "0.1",
  "status": "proposed-category",
  "date_published": "2026-08-01",
  "proposed_by": "Perpensum",
  "canonical_url": "https://perpensum.org/apqv",
  "definition": "Agentic Purchase Quality Verification (APQV) is the continuous, evidence-based process of independently comparing an AI agent's purchase mandate and constraints with the selected source, seller representations, price, transaction, delivery, and buyer-observed outcome, then using observed matches, mismatches, risk signals, and evidence gaps to inform the next purchase decision.",
  "quality_meaning": "Quality means the quality of the purchase as a whole, not the absolute quality of the product or service.",
  "primary_question": "How did the executed purchase and its observed outcome align with the buyer's pre-purchase mandate and constraints?",
  "authorization_boundary": {
    "authorization_question": "Was this agent allowed to make this purchase?",
    "apqv_question": "Did the authorized purchase produce an outcome that aligned with the buyer's pre-purchase conditions?"
  },
  "loop": [
    {
      "stage": "commit",
      "evidence": ["buyer purpose", "constraints", "acceptance criteria"],
      "timing": "before purchase"
    },
    {
      "stage": "snapshot",
      "evidence": ["selected source", "seller representations", "price", "terms"],
      "timing": "before purchase"
    },
    {
      "stage": "observe_transaction",
      "evidence": ["authorization", "order", "payment", "delivery", "retry or refund rights"],
      "timing": "during and after purchase"
    },
    {
      "stage": "observe_outcome",
      "evidence": ["buyer-side usability", "downstream acceptance", "measured result"],
      "timing": "after delivery"
    },
    {
      "stage": "compare_and_return",
      "evidence": ["matches", "mismatches", "risk signals", "evidence gaps", "next-purchase action"],
      "timing": "before the next purchase"
    }
  ],
  "minimal_outcome_states": [
    {
      "id": "usable",
      "meaning": "The observed delivery met the buyer's frozen acceptance criteria for this purpose."
    },
    {
      "id": "delivered_unusable",
      "meaning": "A delivery was observed, but it did not meet the buyer's frozen acceptance criteria."
    },
    {
      "id": "paid_without_delivery",
      "meaning": "Payment was observed, but delivery and a durable recovery path were not."
    },
    {
      "id": "delivery_pending_recoverable",
      "meaning": "Delivery was not observed, but the buyer retains a durable right to retry without repurchasing."
    },
    {
      "id": "insufficient_evidence",
      "meaning": "The available record cannot support a bounded purchase-quality conclusion."
    }
  ],
  "does_not_claim": [
    "absolute product or service quality",
    "buyer satisfaction or emotional value",
    "fraudulent intent from a mismatch alone",
    "seller safety outside the observed evidence",
    "fitness for purposes not committed before purchase",
    "a market benchmark when comparison data is insufficient"
  ],
  "protocol_relationships": [
    {
      "layer": "MCP and A2A",
      "role": "Connect tools and agents and carry task evidence.",
      "apqv_use": "Evidence transport, not the quality judgment itself."
    },
    {
      "layer": "UCP and ACP",
      "role": "Represent commerce objects, checkout, orders, and fulfillment.",
      "apqv_use": "Structured transaction and seller-side fulfillment evidence."
    },
    {
      "layer": "AP2",
      "role": "Provide mandate and payment authorization evidence.",
      "apqv_use": "Evidence that a purchase was authorized, not that its outcome was usable."
    },
    {
      "layer": "Payment and receipt systems",
      "role": "Provide payment, settlement, and response evidence.",
      "apqv_use": "Evidence that value or bytes moved, not that the buyer's purpose was met."
    }
  ],
  "initial_scope": "Machine-verifiable digital outcomes and services, including API results, data extraction, transcription, translation, generation, analysis, and paid agent work.",
  "open_questions": [
    "Which buyer-side observations can be collected without exposing sensitive business data?",
    "Where should an independent quality check sit so that it can affect the next purchase?",
    "Which outcome states remain comparable across different digital purchase purposes?",
    "How should cross-buyer evidence be aggregated without exposing participants or coordinating prices?",
    "When is a mismatch evidence of poor fulfillment, and when is there enough evidence to investigate fraud?"
  ],
  "playground": "https://perpensum.org/playground",
  "source": "https://github.com/perpensum/agent-directed-manipulation/tree/main/apqv",
  "license": "CC-BY-4.0"
}