Microsoft Azure Artifact
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For Agents
Register, retrieve, and ingest machine learning artifacts (models, weights, run outputs) inside Azure Machine Learning workspaces from an AI agent.
Use for: I need to register a trained model artifact under my Azure ML workspace, Retrieve the metadata for a batch of artifacts in one call, Generate a SAS URI to ingest artifacts into a container, Download an artifact content stream by origin and container
Not supported: Does not train models, run inference, or manage compute targets — use for Azure Machine Learning artifact metadata and content management only.
Artifact is the Azure Machine Learning Services API for storing and retrieving the file-based artifacts that ML workflows produce — model files, trained weights, evaluation outputs, and run inputs. It supports per-workspace artifact registration, batch metadata operations, container ingestion via SAS URLs, and content download. Use this API to wire artifact storage into an ML training, evaluation, or deployment pipeline running on Azure ML.
Install Jentic One Beta
Connect the Artifact to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Artifact, 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.
1
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 Artifact API.
Register new artifacts under a workspace and origin/container hierarchy
Retrieve artifact metadata individually or in batches
Ingest artifact content via Azure Blob storage SAS URLs
Download artifact content streams to local or remote consumers
List artifacts within a specific origin and container
Generate write-scoped SAS URIs for batch artifact ingestion
Use Cases
Patterns agents use Artifact API for, with concrete tasks.
★ Training Run Artifact Capture
ML pipelines running on Azure ML produce checkpoints, evaluation reports, and final model files that must be registered against the run for later retrieval. The Artifact API exposes batch register and metadata endpoints to record all outputs of a run in one call, then retrieve the artifact list when promoting the run to a model registry entry.
Generate a containersas URI, upload run outputs to blob storage, then call the batch register endpoint to record artifact metadata against the workspace.
Cross-Pipeline Artifact Retrieval
Downstream evaluation and deployment pipelines need to fetch the model files produced by an upstream training pipeline. Listing artifacts under the producing run's origin and container, then retrieving content by path, lets a deployment script pull exactly the model file it expects without coupling to internal blob storage paths.
List artifacts under the upstream run's origin and container, find the model.bin entry, then GET its content stream and pipe to local disk.
Bulk Artifact Migration
Teams migrating from one Azure ML workspace to another (region change, subscription move) need to copy artifact metadata efficiently. The batch metadata endpoints let a migration script read all artifact records under a workspace origin in chunks, then re-register them in the target workspace with updated container references.
Read batch metadata from the source workspace's artifacts/batch/metadata endpoint, then register the same set against the target workspace using its register endpoint.
Agent-Driven Artifact Lookup via Jentic
An ML ops AI agent triaging a deployment failure can query Jentic to retrieve the exact artifact metadata for the failing model, without holding the Azure ML workspace credentials. Jentic returns the schema for the metadata endpoint and the agent executes against the workspace, parsing the response to identify whether the deployment is missing a required artifact.
Search Jentic for 'get Azure ML artifact metadata', execute against the workspace, origin, and container, then return the artifact list to the agent for inspection.
Key Endpoints
18 endpoints — artifact is the azure machine learning services api for storing and retrieving the file-based artifacts that ml workflows produce — model files, trained weights, evaluation outputs, and run inputs.
METHOD
PATH
DESCRIPTION
POST
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/register
Register a new artifact in the workspace
POST
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/batch/metadata
Register or retrieve artifact metadata in batch
GET
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/metadata
List artifact metadata under the workspace
GET
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/{origin}/{container}
List artifacts under a specific origin and container
POST
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/storageuri/batch/metadata
Get storage URIs for batch artifact metadata
POST
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/register
Register a new artifact in the workspace
POST
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/batch/metadata
Register or retrieve artifact metadata in batch
GET
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/metadata
List artifact metadata under the workspace
GET
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/{origin}/{container}
List artifacts under a specific origin and container
POST
/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/artifacts/storageuri/batch/metadata
Get storage URIs for batch artifact metadata
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Azure AD bearer tokens scoped to the Azure ML workspace are vaulted in Jentic. Agents receive a scoped session at call time and never hold the AAD client secret or workspace key.
Intent-based discovery
Agents search by intent ('register an Azure ML artifact') and Jentic returns the matching operation with its workspace, origin, and container parameter schema, removing the need to navigate the artifact path hierarchy by hand.
Time to first call
Direct integration: 1-2 days for AAD wiring, workspace permission setup, and batch endpoint ergonomics. Through Jentic: under 30 minutes — search, load, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Artifact API through Jentic.
What authentication does the Azure ML Artifact API use?
Azure Active Directory OAuth 2.0 bearer tokens. The token must be authorized for the target Azure ML workspace. Jentic vaults the AAD token and issues a scoped session at call time, so the agent never holds the underlying client secret.
Can I register an artifact from a training run with this API?
Yes. POST to /artifacts/register under the workspace path with the artifact origin, container, and path in the request body. For multiple artifacts, use /artifacts/batch/metadata to register a list in a single call, which is the typical pattern for end-of-run capture.
What are the rate limits for the Azure ML Artifact API?
Standard Azure ML data-plane throttling applies. Limits are not exposed in the spec; in practice the batch metadata endpoints handle hundreds of artifacts per call efficiently, while individual register and content endpoints are subject to per-workspace concurrency caps. Use batch endpoints when registering more than a handful of artifacts.
How do I download an artifact's content through Jentic?
Search Jentic for 'download Azure ML artifact content'. Jentic returns the schema for GET /artifacts/{origin}/{container}/{path} (content stream); execute it with the workspace, origin, and container path. The response is the binary content of the artifact file.
Is the Azure ML Artifact API free?
Calls themselves are not separately billed. Storage of artifacts uses the workspace's associated Azure Storage account, which is billed by GB-month and transaction count under standard Blob storage pricing.
How do I ingest a whole container of artifacts at once?
POST to /artifacts/{origin}/{container}/batch/ingest/containersas to receive a write-scoped SAS URI for the target container, upload the files using a standard Blob client, then register the metadata via /artifacts/batch/metadata. This pattern is the most efficient for end-of-run uploads.
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