Azure Anomaly Finder Client

Name: Azure Anomaly Finder Client API
Brand: Azure Anomaly Finder Client
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl InferenceapiKey2 EndpointsREST

For Agents

Score a numeric time series for anomalies in batch or detect whether the most recent point is an outlier given prior history.

Connect the Azure Anomaly Finder Client to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Azure Anomaly Finder Client, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Azure Anomaly Finder Client API.

Detect anomalies across an entire time series in a single batch call

Score only the latest point in a series against a model trained on prior values

Configure sensitivity and granularity (hourly, daily) per request

Return upper, lower, and expected values alongside each anomaly flag

GET STARTED

Start building with Azure Anomaly Finder Client API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Azure Anomaly Finder Client, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Azure Anomaly Finder Client, keeping it validated and agent-ready. The API runs unsupervised anomaly detection on numeric time series data. Two operations cover the common modes: batch detection over an entire series with a model trained on the supplied points, and last-point detection that scores only the most recent value against a model trained on the prior history. It is a building block for incident detection, fraud signals, and capacity-planning alerts.

Use Cases

Patterns agents use Azure Anomaly Finder Client API for, with concrete tasks.

★ Operational Metrics Outlier Detection

SRE teams stream KPIs (latency, error rate, throughput) and call /timeseries/last/detect to score the most recent point against the prior window. The API returns isAnomaly, expectedValue, and upper/lower bounds, so an alert pipeline can fire only on statistically significant deviations rather than threshold-based rules.

POST /timeseries/last/detect with the last 168 hourly latency points and granularity=hourly, then alert if isAnomaly is true

Backfill Anomaly Scoring on Historical Series

Analytics teams reviewing a quarter of business KPIs use /timeseries/entire/detect to score the full series in one call. The response flags each point as anomaly or not, supporting offline incident review and cohort analysis without standing up a custom ML pipeline.

POST /timeseries/entire/detect with 90 daily revenue points and granularity=daily and return all indices flagged as anomalies

Sensitivity Tuning for Noisy Signals

Some signals (consumer foot traffic, spiky API calls) are inherently bursty. The sensitivity parameter on both endpoints lets a caller widen or tighten the expected band so that genuine anomalies surface and routine spikes are ignored. This avoids alert fatigue without retraining a custom model.

Run /timeseries/entire/detect with sensitivity=85 and compare flagged points against the default sensitivity=99 result

Agent-Driven Incident Triage

An AI agent reviewing an alert can pull the relevant metric series, call Anomaly Finder, and decide whether the spike is a true outlier before paging a human. Through Jentic, intent search returns the right detect endpoint with its input schema, the API key is held in the credential vault, and the agent gets back a clean isAnomaly verdict in one call.

Search Jentic for 'detect anomaly in time series', load /timeseries/last/detect, and execute against the most recent 24 points with granularity=hourly

Key Endpoints

2 endpoints — jentic publishes the only available openapi specification for azure anomaly finder client, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/timeseries/entire/detect

Detect anomalies across an entire series

POST

/timeseries/last/detect

Detect whether the latest point is an anomaly

POST

/timeseries/entire/detect

Detect anomalies across an entire series

POST

/timeseries/last/detect

Detect whether the latest point is an anomaly

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

Cognitive Services subscription keys are stored encrypted in the Jentic vault (MAXsystem) and injected into the Ocp-Apim-Subscription-Key header at execution time. Agents receive scoped invocation, never the raw key.

Intent-based discovery

Agents search by intent (e.g., 'detect anomaly in time series') and Jentic returns the matching detect operation with its input schema, so the agent calls the right endpoint without browsing Cognitive Services docs.

Time to first call

Direct integration: half a day to a day for key handling, request shaping, and retry handling. Through Jentic: under 1 hour — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Azure Cognitive Services Management Client

Provisions and manages the Cognitive Services account whose key authenticates Anomaly Finder calls.

Use the management client to provision the resource and rotate keys; use Anomaly Finder for the actual scoring.

Complementary

Azure Monitor Management Client

Source of the metric series often passed to Anomaly Finder for scoring.

Pull metrics from Monitor, then score them with Anomaly Finder before alerting.

Complementary

Azure Application Insights Management Client

Collects custom telemetry that can be passed to Anomaly Finder as input.

Combine App Insights queries with Anomaly Finder when the input series comes from application telemetry.

Alternative

Google Cloud Monitoring API

GCP equivalent for metric storage and anomaly-style alert policies.

Choose Cloud Monitoring on GCP. Stay with Anomaly Finder for an Azure or model-driven detection step.

FAQs

Specific to using Azure Anomaly Finder Client API through Jentic.

Why is there no official OpenAPI spec for Azure Anomaly Finder Client?

Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Azure Anomaly Finder Client via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Azure Anomaly Finder API use?

The API uses an Azure Cognitive Services subscription key passed in the Ocp-Apim-Subscription-Key header (apiKeyHeader scheme). Through Jentic, the key is stored encrypted in the MAXsystem vault and injected at execution time, so agents never see the raw secret.

Can I detect anomalies on streaming metrics with this API?

Yes. Use POST /timeseries/last/detect on each cycle with the recent window of points; the API trains on the supplied prior values and scores the last point. The call is stateless, so you control the window and granularity per request.

What time series granularities are supported?

The endpoints accept the granularity field in the request body. Common values are hourly, daily, and minutely. The granularity must match the spacing of the supplied points; mixing densities produces unstable scores.

What are the rate limits for the Azure Anomaly Finder API?

Limits are tied to the Cognitive Services pricing tier of the resource backing the subscription key. Free F0 tiers cap at low transactions per second; standard tiers go higher. Honour HTTP 429 Retry-After values and batch with /timeseries/entire/detect when scoring long histories.

How do I run anomaly detection on a metric through Jentic?

Run pip install jentic, then search Jentic with 'detect anomaly in time series'. Jentic returns POST /timeseries/last/detect with its input schema. Load the schema, execute with your point series and granularity, and act on isAnomaly. The Cognitive Services key is resolved from the vault automatically.