Run History APIs

Name: Run History APIs API
Brand: Run History APIs
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth226 EndpointsREST

For Agents

Query Azure Machine Learning experiments, runs, metrics, artifacts, and events to reconstruct training history and feed downstream model promotion workflows.

Connect the Run History APIs to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Run History APIs, 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 Run History APIs API.

Query runs across an experiment by tags, status, or time window

Log batches of metrics or events to an in-progress run

List the artifact tree produced by a run including content URIs

Inspect run details and child runs for hyperparameter search hierarchies

GET STARTED

Start building with Run History APIs API

Explore with Jentic

View OpenAPI Document

Jentic publishes the only available OpenAPI document for Run History APIs, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for the Azure Machine Learning Run History APIs, keeping it validated and agent-ready. The Run History service tracks experiments, runs, metrics, artifacts, events, and tags inside an Azure Machine Learning workspace, giving teams a queryable record of every training and evaluation execution. Agents can query runs by tag or experiment, log metrics in batches, list run children for hierarchical workflows, and fetch artifact metadata for downstream packaging or serving. It is the system of record behind Azure ML experiment tracking.

Use Cases

Patterns agents use Run History APIs API for, with concrete tasks.

★ Experiment Tracking and Run Comparison

Data science teams query the Run History API to compare metrics across runs of an experiment when picking a model to promote. The runs:query endpoint returns matching runs filtered by tag or status, and the metrics endpoints return per-run scalar values. This is the backbone of any model leaderboard built on top of Azure ML.

POST runs:query for experiment churn-baseline filtered by status=Completed and return the runId and final accuracy metric for each match

Streaming Metric and Event Logging

Training jobs send metric and event batches back to the Run History service while running. The batch endpoints accept arrays of metric and event objects so a long training loop can flush them periodically without one-call-per-step overhead. This keeps the experiment timeline up to date without saturating the control plane.

POST a batch of 50 metric records and 10 event records to runs/{runId}/batch/metrics for the active training run

Artifact Discovery for Model Promotion

When promoting a trained model from a winning run, the agent needs the full artifact tree the run produced — typically the model file, evaluation report, and signature. The artifacts endpoints list paths, content URIs, and metadata so the agent can copy the right files into the registry without scanning the workspace blob store directly.

GET runs/{runId}/artifacts and then GET artifacts/contentinfo for each artifact path to retrieve the SAS URLs for downloading

Agent-Driven Run Triage via Jentic

Through Jentic, an MLOps agent can search for query azure ml runs, load the runs:query input schema, execute it with experiment and tag filters, then iterate to fetch metrics for the top runs. The Azure OAuth token is managed by Jentic, so the agent never holds a workspace credential directly.

Search Jentic for query azure ml runs, load the runs:query schema, execute it for experiment churn-baseline filtered by tag promote=candidate, then call client.execute on the metrics endpoint for each returned runId

Key Endpoints

26 endpoints — jentic publishes the only available openapi specification for the azure machine learning run history apis, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/history/v1.0/.../experiments/{experimentName}/runs:query

Query runs by filter

GET

/history/v1.0/.../experiments/{experimentName}/runs/{runId}

Get a single run

GET

/history/v1.0/.../experiments/{experimentName}/runs/{runId}/metrics

List metrics for a run

POST

/history/v1.0/.../experiments/{experimentName}/runs/{runId}/batch/metrics

Log metrics in batch

GET

/history/v1.0/.../experiments/{experimentName}/runs/{runId}/artifacts

List artifacts produced by a run

GET

/history/v1.0/.../experiments/{experimentName}/runs/{runId}/children

List child runs

POST

/history/v1.0/.../experiments/{experimentName}/runs:query

Query runs by filter

GET

/history/v1.0/.../experiments/{experimentName}/runs/{runId}

Get a single run

GET

/history/v1.0/.../experiments/{experimentName}/runs/{runId}/metrics

List metrics for a run

POST

/history/v1.0/.../experiments/{experimentName}/runs/{runId}/batch/metrics

Log metrics in batch

GET

/history/v1.0/.../experiments/{experimentName}/runs/{runId}/artifacts

List artifacts produced by a run

Why though Jentic?

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

Credential isolation

Azure service principal credentials are stored encrypted in the Jentic vault. Agents receive scoped OAuth tokens for the Azure ML audience only — the underlying client secret never enters the agent context.

Intent-based discovery

Agents search by intent such as query azure ml runs or log ml metrics, and Jentic returns the matching Run History operation with its full input schema, including the deeply nested workspace path parameters.

Time to first call

Direct integration: 2-4 days to handle MSAL, batch payload shapes, and artifact SAS retrieval. Through Jentic: under an hour to search, load, and execute a run query.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Azure ML Web Services Management Client

Deploys models trained in runs as scoring web services

Use after picking a winning run to promote its model into a deployed endpoint

Complementary

Azure ML Team Account Management Client

Manages workspaces and team accounts that own these experiments

Use to provision the workspace where Run History will then track experiments

Complementary

Azure ML Commitment Plans

Controls capacity and pricing for ML workloads

Use to manage commitment plans before scaling experiment compute

FAQs

Specific to using Run History APIs API through Jentic.

Why is there no official OpenAPI spec for the Run History APIs?

Microsoft Azure does not publish a unified OpenAPI specification for the Azure Machine Learning Run History service. Jentic generates and maintains this spec so that AI agents and developers can call Run History via structured tooling. It is validated against the live Azure ML API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Run History API use?

It uses Azure Active Directory OAuth 2.0 via the azure_auth flow defined in the spec. Acquire a bearer token for the Azure ML service audience and pass it as Authorization: Bearer. Jentic stores the underlying service principal in its vault so the agent only sees a scoped token.

Can I query runs by tag with the Run History API?

Yes. POST to .../experiments/{experimentName}/runs:query with a filter expression that matches tag keys and values. The response is a paginated list of run objects including runId, status, and metrics references.

How do I log metrics from a long-running training job?

Use the batch endpoint POST .../runs/{runId}/batch/metrics with an array of metric records. Batching avoids one-call-per-step overhead and keeps the experiment timeline current. Events follow the same pattern at /batch/events.

What are the rate limits for the Run History API?

Azure ML applies workspace-scoped throttling. Heavy ingestion of metrics and events is expected to use the batch endpoints. Throttled requests return HTTP 429 with a Retry-After header. For sustained logging, batch sizes of 50-100 records per call are typical.

How do I fetch artifact URIs through Jentic?

Search Jentic for list azure ml run artifacts, load the GET artifacts schema, and execute it for the target runId. Then call client.execute on the artifacts/contentinfo operation to receive the SAS URLs the agent needs to download model files.