Microsoft Azure Artifact
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AI ReadinessFoundational (D+)50/100
Community OpenAPI document · agent-readyAI/MLMl Inferenceoauth218 EndpointsREST
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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://jentic.com/install.sh?src=apis&api=%2Fapis%2Fazure.com%2Fartifact" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Fazure.com%2Fartifact" | 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
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
A
Artifact
- Foundational (D+)50/100
64
Foundational Compliance63
Developer Experience & Jentic Compatibility30
AI-Readiness & Agent Experience94
Agent Usability35
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
64
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: BSignals: 4
94%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
0%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
60%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
30
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
66%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
0%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
53%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
35
Security
Trust, risk posture, and security compliance.
Grade: FSignals: 1
35%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring the Azure Machine Learning Artifact API by hand means registering an Azure AD app authorized for the target workspace, acquiring and refreshing OAuth 2.0 bearer tokens, and coordinating the container SAS URI, blob upload, and batch metadata register steps yourself. Through Jentic you install once, import the Azure ML Artifact API from the API Directory, store the service principal credential once, and your agent calls it.
Permission scoping
Workspaces sit in the path (/artifact/v2.0/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/...) while the artifact origin, container, and path arrive in the request body, so scoping combines a workspace pin with operation choice. Pin the agent to one workspace and limit it to the operations it needs, such as listing artifact metadata or downloading content, so it never gains a register or ingest operation you did not grant.
Credential isolation
Your Azure AD service principal credential, scoped to the ML workspace, is stored once, encrypted, by your own Jentic One instance and exchanged for a short-lived bearer token at execution time. The secret never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'register an Azure ML artifact' or 'download artifact content by origin and container', and Jentic returns the matching operation with its workspace, origin, and container schema so the agent calls it without walking the artifact path hierarchy.
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 securely stores 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.
Can I limit what my agent is allowed to do with the Microsoft Azure ML Artifact API?
Yes. Because you run Jentic One yourself, your own rules decide which operations and credentials the agent may use, so you pin it to a single Azure ML workspace and grant only the operations it needs. For a read-only agent you allow just listing artifact metadata and downloading artifact content by origin and container, while withholding the register and container-ingest operations. The service principal credential stays scoped to that one workspace, so the agent cannot write artifacts or reach a workspace you did not grant.
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