{"uid":"cap__GTnQwjJgc_Yn_S-Aw0R4","slug":"aient-ai-09b34ee7","name":"Aient Anomaly Detection","description":"Detect statistical anomalies across error rate, latency, or throughput grouped by service or operation — the same algorithm Aient uses internally to spawn problems. Pricier than raw queries because it runs windowed regression.","url":"https://aient.ai/x402/anomalies","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"since":{"type":"string","format":"date-time"},"until":{"type":"string","format":"date-time"},"metric":{"enum":["error_rate","duration_p99","throughput"],"type":"string"},"groupBy":{"enum":["service","operation"],"type":"string"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.010000","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"settled","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.010000/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_EMHn_OHO98OorJDO_sibs","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Detects statistical anomalies in error rate, latency, or throughput for your services using windowed regression — the same algorithm Aient uses internally to identify problems.","exampleAgentPrompt":"Can you run anomaly detection on my Aient-monitored services and flag any statistical outliers in error rate or latency from the last 2 hours, grouped by service?","exampleUseCases":null,"resultDescription":"Returns a list of detected anomalies grouped by service or operation, each with the metric type (error_rate, latency, or throughput), the anomalous window, an anomaly score, and contextual statistics from the windowed regression model.","failureModes":["Invalid or missing ingest/API key — 401 Unauthorized","No telemetry data ingested for the requested time range — empty results or 404","Invalid metric type or grouping field — 400 Bad Request","Insufficient data points for regression — model may return no anomalies or a specific error","Rate limiting or quota exceeded — 429 Too Many Requests","Malformed request body — 422 Unprocessable Entity"],"whenToPreferThis":"Use this endpoint when you need statistically rigorous anomaly detection rather than simple threshold alerts — specifically when you want the same algorithm Aient uses internally to spawn problems. Prefer this over raw trace queries when you want pre-computed regression-based outlier detection across error rate, latency, or throughput dimensions grouped by service or operation.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T00:30:22.555Z","isFirstParty":false}