Microsoft Azure Run History APIs
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AI ReadinessFoundational (D)47/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl Inferenceoauth226 EndpointsREST
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For Agents
Query Azure Machine Learning experiments, runs, metrics, artifacts, and events to reconstruct training history and feed downstream model promotion workflows.
Use for: Find all runs of an experiment with status Completed, Get the metrics logged for a specific run id, List the artifacts produced by an Azure ML run, I want to query runs by a custom tag
Not supported: Does not train models, deploy scoring endpoints, or manage compute clusters - use for tracking and querying ML experiments, runs, metrics, and artifacts only.
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.
Install Jentic One Beta
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.
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%2Frun-history-apis" | 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%2Frun-history-apis" | 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 Run History APIs.
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
Update tags on experiments and runs to drive promotion workflows
Delete experiment ids that are no longer needed in the workspace
Use Cases
Patterns agents use Run History APIs 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
GET
/history/v1.0/.../experiments/{experimentName}/runs/{runId}/children
List child runs
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.
R
Run History APIs
- Foundational (D)47/100
55
Foundational Compliance63
Developer Experience & Jentic Compatibility26
AI-Readiness & Agent Experience94
Agent Usability35
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
55
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: CSignals: 4
91%
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).
30%
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.
26
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
53%
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 ML Run History APIs by hand means registering an Azure AD app, running the OAuth2 azure_auth token exchange for the Azure ML audience, and constructing the deeply nested workspace path and batch metric payloads yourself. Through Jentic you install once, import Run History APIs from the API Directory, store the Azure service principal credential once, and your agent calls it.
Permission scoping
Run History puts the experiment and run identifiers in the URL path (.../experiments/{experimentName}/runs/{runId}/...), so a rule can pin your agent to reading and logging within a given experiment. You choose the operations it may call, so destructive ones like deleting an experiment are not included unless you add them.
Credential isolation
Your Azure service principal credential is stored once, encrypted, by your own Jentic One instance and injected at execution time. It never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'query Azure ML runs by experiment' or 'log training metrics in batch', and Jentic returns the matching Run History operation with its input schema, including the nested workspace and experiment path parameters.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Run History APIs 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 with Jentic One, the self-hosted execution layer.
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.
Can I limit what my agent is allowed to do with the Run History API?
Yes. Because Jentic One is self-hosted, you decide which Run History operations the agent may call, so read and log actions like querying runs, fetching metrics, and listing artifacts can be enabled while destructive ones such as deleting an experiment stay off unless you add them. Since the service puts the experiment and run identifiers in the URL path, your own rules can also pin the agent to reading and logging within a specific experiment. The Azure service principal credential is held by your instance and injected only at execution time, so the agent works within the scope you set.
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