Anomaly Detection · Agentic Access

Anomaly Detection Agentic Access

x-agentic-access generated

Anomaly Detection exposes 9 API operations that an AI agent could call, of which 7 are state-changing ‘acting’ operations. This is a recommended x-agentic-access execution contract — the scope, audience, consequence tier, short-lived token constraints, and escalation each action should carry before it is handed to an autonomous agent.

By consequence: 2 read and 7 write.

Contracts are classified heuristically from the provider’s OpenAPI and refresh on every APIs.io network build; audience is bound per deployment. The model follows Curity’s Access Intelligence (apidays Munich 2026). Browse every provider’s agent contracts at agentic-access.apis.io.

Anomaly DetectionArtificial IntelligenceData ScienceFraud DetectionMachine LearningMonitoringObservabilityOutlier DetectionPattern RecognitionSecurityTime Series

Operations: 9 Acting: 7 Human-in-the-loop: 0 Method: generated

By consequence

read 2 write 7

Source

Agentic Access

generated: '2026-07-15'
method: generated
source: openapi/anomaly-detection-openapi.yml
description: Recommended x-agentic-access execution contracts, classified heuristically from
  the OpenAPI. A governance starting point for exposing this API to AI agents — review and bind
  audience per deployment. See research/curity/agentic-governance/.
summary:
  operations: 9
  by_action_class:
    acting: 7
    connected: 2
  by_consequence:
    write: 7
    read: 2
  human_in_the_loop_required: 0
operations:
- path: /timeseries/entire/detect
  method: post
  operationId: detectUnivariateEntireSeries
  x-agentic-access:
    action-class: acting
    consequence: write
    subject: required
    audience: null
    token:
      max-ttl: 900
    escalation:
      human-in-the-loop: conditional
      triggers:
      - abnormal
      - high-value
    audit: required
- path: /timeseries/last/detect
  method: post
  operationId: detectUnivariateLastPoint
  x-agentic-access:
    action-class: acting
    consequence: write
    subject: required
    audience: null
    token:
      max-ttl: 900
    escalation:
      human-in-the-loop: conditional
      triggers:
      - abnormal
      - high-value
    audit: required
- path: /timeseries/changepoint/detect
  method: post
  operationId: detectUnivariateChangePoint
  x-agentic-access:
    action-class: acting
    consequence: write
    subject: required
    audience: null
    token:
      max-ttl: 900
    escalation:
      human-in-the-loop: conditional
      triggers:
      - abnormal
      - high-value
    audit: required
- path: /multivariate/models
  method: get
  operationId: listMultivariateModels
  x-agentic-access:
    action-class: connected
    consequence: read
    subject: optional
    token:
      max-ttl: 3600
    audit: none
- path: /multivariate/models
  method: post
  operationId: trainMultivariateModel
  x-agentic-access:
    action-class: acting
    consequence: write
    subject: required
    audience: null
    token:
      max-ttl: 900
    escalation:
      human-in-the-loop: conditional
      triggers:
      - abnormal
      - high-value
    audit: required
- path: /multivariate/models/{modelId}
  method: get
  operationId: getMultivariateModel
  x-agentic-access:
    action-class: connected
    consequence: read
    subject: optional
    token:
      max-ttl: 3600
    audit: none
- path: /multivariate/models/{modelId}
  method: delete
  operationId: deleteMultivariateModel
  x-agentic-access:
    action-class: acting
    consequence: write
    subject: required
    audience: null
    token:
      max-ttl: 900
    escalation:
      human-in-the-loop: conditional
      triggers:
      - abnormal
      - high-value
    audit: required
- path: /multivariate/models/{modelId}:detect-batch
  method: post
  operationId: detectMultivariateBatchAnomaly
  x-agentic-access:
    action-class: acting
    consequence: write
    subject: required
    audience: null
    token:
      max-ttl: 900
    escalation:
      human-in-the-loop: conditional
      triggers:
      - abnormal
      - high-value
    audit: required
- path: /multivariate/models/{modelId}:detect-last
  method: post
  operationId: detectMultivariateLastAnomaly
  x-agentic-access:
    action-class: acting
    consequence: write
    subject: required
    audience: null
    token:
      max-ttl: 900
    escalation:
      human-in-the-loop: conditional
      triggers:
      - abnormal
      - high-value
    audit: required