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Synthetic tests run a simulated autonomous-agent checkout against a merchant and report exactly where the agent would succeed or fail: discovery, selection, cart add, checkout form, payment. Useful for quantifying integration gaps before investing engineering time.

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POST /v1/scanner/tests

Scope required: tests   Cost: 5 credits 
curl -X POST https://scanner.getsly.ai/v1/scanner/tests \
  -H "Authorization: Bearer $SCANNER_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "domain": "example-merchant.com",
    "test_type": "full_flow"
  }'
Request fields:
FieldValues
domainRequired
test_typebrowse, search, add_to_cart, checkout, full_flow (default)
Test types run a subset of steps — use browse for a quick check, full_flow for a complete checkout simulation. Response is an AgentShoppingTestResult with per-step status and a blockers array flagging specific friction points (missing structured data, CAPTCHA, account requirement, unsupported payment method, etc.).

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GET /v1/scanner/tests/:domain

Retrieve previous test results for a domain. Scope required: read   Cost: 0 
curl -H "Authorization: Bearer $SCANNER_KEY" \
  "https://scanner.getsly.ai/v1/scanner/tests/example-merchant.com?limit=5"
Returns the 5 most recent tests in descending tested_at order.

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Interpreting results

A test with status: "passed" and success_rate: 100 means the agent completed every step. More common is status: "partial" or "failed" with populated blockers — these are the actionable findings. Blocker severity:
  • blocking — the agent cannot continue. Common examples: no_structured_data, captcha_required, account_required, javascript_wall.
  • warning — the agent continued but with degraded confidence (e.g., inferred price from OCR instead of a schema.org price field).
failure_point on the result root tells you exactly where the run stopped: 
{
  "failure_point": {
    "step": "payment",
    "blocker": "unsupported_payment_method",
    "detail": "Only accepts AMEX/Discover; agent wallet supports Visa/MC only"
  }
}

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Economics metadata

When the merchant has category data, the test enriches the result with: 
{
  "estimated_monthly_agent_visits": 1200,
  "estimated_lost_conversions": 36,
  "estimated_lost_revenue_usd": 2700
}
These are heuristic projections based on merchant-category baselines (visits, AOV, conversion rate) multiplied by the test’s failure rate. Use as directional signal, not as revenue forecasting.