Execution Service

Name: Execution Service API
Brand: Azure ML Execution Service
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferenceoauth24 EndpointsREST

For Agents

Start training runs (remote, snapshot, or local) against an Azure ML workspace experiment and cancel a running run by id.

Connect the Execution Service to your agent

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

Start a training run on a remote compute target inside an Azure ML workspace experiment

Start a training run from an existing snapshot on a remote compute target

Start a training run on the local machine for an experiment

Cancel a training run by run id within a workspace experiment

GET STARTED

Start building with Execution Service API

Explore with Jentic

View OpenAPI Document

Use for: Start a training run on remote compute aml-cpu-cluster for experiment forecasting-v2, Cancel run abc123 in experiment forecasting-v2 in workspace ml-prod, Start a snapshot run from snapshot id snap-456 on remote compute aml-gpu, Trigger a local run for experiment churn-model in workspace ml-dev

Not supported: Does not register models, manage datasets, provision compute targets, or read run logs and artefacts — use only to start (remote, snapshot, local) and cancel Azure ML training runs.

Jentic publishes the only available OpenAPI document for Execution Service, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for the Azure Machine Learning Execution Service, keeping it validated and agent-ready. This narrow API starts and cancels training runs against an Azure Machine Learning workspace experiment. It supports starting a remote run, starting a run from a snapshot on a remote compute target, starting a local run, and cancelling a run by run id.

Use Cases

Patterns agents use Execution Service API for, with concrete tasks.

★ Trigger Remote Training Runs

Data science teams kick off training jobs on a remote compute cluster from an external orchestrator (CI, scheduler, or agent). POST /execution/v1.0/.../experiments/{experimentName}/startrun submits the run definition to the Azure ML workspace and returns a run id, which the orchestrator stores for later status polling and artefact retrieval against the workspace data plane.

Start a remote run for experiment forecasting-v2 in workspace ml-prod targeting compute aml-cpu-cluster and return the run id

Snapshot-Based Reproducible Runs

ML platform teams use snapshot runs to rerun a previously captured code and config bundle on different compute, ensuring exact reproducibility across reruns. POST /execution/v1.0/.../experiments/{experimentName}/snapshotrun starts the run from a saved snapshot id so reruns of past experiments do not depend on the current working directory.

Start a snapshot run from snapshot snap-456 for experiment fraud-detect on remote target aml-gpu-cluster

Cancel a Long-Running Job

Operators cancel a training run that is no longer needed (wrong config, rebased dataset, manual intervention) without waiting for it to fail or time out. POST /execution/v1.0/.../runId/{runId}/cancel signals the orchestrator to stop the run, freeing up compute capacity on the cluster for queued jobs.

Cancel run abc123 in experiment forecasting-v2 in workspace ml-prod and confirm the cancellation was accepted

Agent-Driven Experiment Orchestration

An AI agent running scheduled retraining can submit, monitor, and cancel runs on Azure ML through Jentic without holding the AAD service principal secret. Intent search returns the four execution endpoints with their schemas; the OAuth token is materialised from the Jentic vault per call so the agent only ever holds a short-lived bearer.

Search Jentic for 'start an Azure ML training run', execute Runs_StartRun for experiment forecasting-v2, and cancel any older active run on the same experiment

Key Endpoints

4 endpoints — jentic publishes the only available openapi specification for the azure machine learning execution service, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/execution/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/startrun

Start a run on a remote compute target

POST

/execution/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/snapshotrun

Start a run from a snapshot on a remote compute target

POST

/execution/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/startlocalrun

Start a run on a local machine

POST

/execution/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/runId/{runId}/cancel

Cancel an active run

POST

/execution/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/startrun

Start a run on a remote compute target

POST

/execution/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/snapshotrun

Start a run from a snapshot on a remote compute target

POST

/execution/v1.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/experiments/{experimentName}/startlocalrun

Start a run on a local machine

POST

Cancel an active run

Why though Jentic?

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

Credential isolation

Azure AD OAuth 2.0 tokens are minted and rotated inside the Jentic vault (MAXsystem). Service principal client secrets stay encrypted; agents call Microsoft.MachineLearningServices with a short-lived scoped token only.

Intent-based discovery

Agents search by intent (e.g., 'start an Azure ML training run') and Jentic returns Runs_StartRun (or the snapshot, local, or cancel variants) with its input schema, so the agent does not have to know the long workspace path.

Time to first call

Direct Azure ML integration: 1-2 days for AAD setup, workspace path construction, and run lifecycle handling. Through Jentic: under 30 minutes — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Complementary

Azure Machine Learning Workspaces Management

Provisions and configures the workspaces that this Execution Service submits runs against.

Use the workspaces management client to create the workspace; use this Execution Service to run experiments inside it.

Complementary

Azure Machine Learning Workspaces

Manages compute targets, datastores, and registered models within an Azure ML workspace.

Use this API to manage the compute and datasets the Execution Service runs jobs on.

Alternative

Amazon SageMaker

AWS managed ML platform with equivalent training-job submission and lifecycle APIs.

Choose SageMaker for AWS-native ML training. Stay with Execution Service when training inside an Azure ML workspace.

Alternative

Google Cloud AI Platform Training

Google Cloud equivalent for submitting and managing managed training jobs.

Choose AI Platform Training for GCP workloads. Stay with Execution Service for Azure ML workspace runs.

FAQs

Specific to using Execution Service API through Jentic.

Why is there no official OpenAPI spec for Execution Service?

Microsoft Azure does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Execution Service 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 ML Execution Service use?

The API uses Azure Active Directory OAuth 2.0 bearer tokens (the azure_auth scheme). Through Jentic, the AAD service principal secret is stored encrypted in the MAXsystem vault and exchanged for a short-lived bearer token per call to the workspace.

Can I start a training run on Azure ML with this API?

Yes. Call POST /execution/v1.0/.../experiments/{experimentName}/startrun with the run definition to submit a remote run. Use snapshotrun to start from a saved snapshot id, or startlocalrun to run on the local compute. The response returns a run id you can use to track the run.

How do I cancel a training run that is already in progress?

Send POST /execution/v1.0/.../experiments/{experimentName}/runId/{runId}/cancel with the run id. The orchestrator marks the run for cancellation; you can then poll the workspace data plane to confirm it has reached a terminal state.

What are the rate limits for the Execution Service?

Azure Machine Learning enforces per-workspace throttling. Submitting many runs in tight loops can return HTTP 429 — back off using the Retry-After header. There is no documented per-endpoint quota in this preview spec; treat the rate as workspace-wide.

How do I trigger an Azure ML run from an agent through Jentic?

Run pip install jentic, then search Jentic with 'start an Azure ML training run'. Load Runs_StartRun, supply the subscription, resource group, workspace, and experiment names along with the run definition, and execute. The AAD token is sourced from the vault at call time so the agent never sees the service principal secret.