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SignalHarness AI Search Pipeline Regression Report
x402
SignalHarness AI Search Pipeline Regression Report is a paid API for AI agents from signalharness.ai, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-13).
Analyzes a caller-supplied AI search pipeline configuration or result set and returns a regression report identifying issues or anomalies.
Price / call$0.05
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object containing a regression analysis report (analysis_json), any warnings about the data quality or reliability, the evidence scope indicating data provenance, and a signed receipt with execution metadata including payment ID, latency, and a SHA-256 result hash for auditability.
Example use cases
Search quality regression detection
“Here's the output from my AI search pipeline for the last run — can you analyze it for regressions and tell me if anything has degraded compared to what's expected? The data is: {"pipeline":"product-search","run_id":"run_2024_05","queries_evaluated":500,"avg_precision":0.71}”
Pre-release search pipeline audit
“Before we ship our new search ranking model, run a regression report on this pipeline evaluation JSON and highlight any anomalies or warnings I should address: {"pipeline":"ranking-v3","baseline_precision":0.85,"current_precision":0.78,"num_queries":200}”
Scheduled search pipeline health check
“I've got my weekly AI search pipeline metrics here — run them through the regression report tool and tell me if anything looks off: {"pipeline":"enterprise-search","week":"2024-W20","recall":0.88,"precision":0.82,"latency_p99_ms":340}”
Technical details
Endpoint
POSThttps://signalharness.ai/api/agent/services/ai_search_pipeline_regression_report/invokeWhen to prefer
Choose this endpoint when you need a fast, pay-per-call regression analysis of an AI search pipeline without managing your own evaluation infrastructure. It is best suited for agents that need a portable, auditable report on search quality degradation, especially when operating within a USDC micropayment workflow on Base. Prefer alternatives if you need real-time streaming analysis, integration with a specific MLOps platform, or deeply customized regression metrics beyond what a JSON payload can describe.
Side effects
- Malformed or invalid request_json input returns an error or empty analysis
- Caller-supplied data that does not conform to expected pipeline schema may produce unreliable results with warnings
- Network or payment authorization failure results in a non-200 response with no analysis
- Very large JSON payloads near the 65536-character limit may be rejected or time out
- Ambiguous pipeline data may result in low-confidence analysis with prominent warnings
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| request_json | string |
Output
Schema · Response
{
"type": "json",
"example": {
"replay": false,
"result": {
"warnings": [
"Verify the caller-supplied data before relying on this result."
],
"service_id": "ai_search_pipeline_regression_report",
"analysis_json": "{\"example\":\"schema-valid caller-supplied data\"}",
"evidence_scope": "caller_supplied_data"
},
"status": "succeeded",
"receipt": {
"asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
"usage": [],
"status": "succeeded",
"network": "eip155:8453",
"artifacts": [],
"endedAtMs": 0,
"latencyMs": 0,
"paymentId": "example-payment",
"receiptId": "example-receipt",
"requestId": "example-request",
"serviceId": "ai_search_pipeline_regression_report",
"executionId": "example-execution",
"startedAtMs": 0,
"amountAtomic": "50000",
"resultSha256": "7747538e3e202e7ddccde7e5f678bf09f59162f013fb9ac54e30d918be091634",
"serviceVersion": "1.0.0",
"settlementReference": "0x0000000000000000000000000000000000000000000000000000000000000000"
},
"artifacts": [],
"requestId": "example-request",
"executionId": "example-execution"
}
}Tags
x402
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