Microsoft Azure Azure Anomaly Finder Client
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For Agents
Score a numeric time series for anomalies in batch or detect whether the most recent point is an outlier given prior history.
Use for: Detect anomalies across the last 30 days of CPU utilisation, Check whether the latest 5-minute revenue point is an outlier, Score an hourly time series of API error counts for anomalies, Find anomalies in a daily order volume series for the last quarter
Not supported: Does not store time series, train custom models, or trigger downstream alerts — use only to score supplied numeric series for anomalies.
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.
Install Jentic One Beta
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.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
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Step 1: 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 | sh2
Step 2: 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 instanceJentic 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
Operate stateless: the model is trained from the points supplied per call
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 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.
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.
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