Install Jentic One Beta

Connect the Arthur Scope to your agent

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

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%2Farthur.ai%2Farthur" | sh

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%2Farthur.ai%2Farthur" | 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 Arthur Scope API.

Publish metric versions and upload their values

Query model metrics over time

Create and manage model alerts and alert rules

Organise models with workspaces and connectors

GET STARTED

Start building with Arthur Scope API

Explore with Jentic One

View OpenAPI Document

Alert on model drift

Catch a degrading model early. The agent defines alert rules on a model and reviews the alerts that fire so a drifting model gets flagged for a human.

Call GET /api/v1/models/{model_id}/alert_rules, then GET /api/v1/models/{model_id}/alerts

Organise monitored models

Keep monitoring tidy across teams. The agent groups models into workspaces and wires connectors to the datasets feeding them.

Call GET /api/v1/workspaces/{workspace_id} to review a workspace and its models

Key Endpoints

202 endpoints — arthur scope is the monitoring and observability api for production machine learning.

METHOD

PATH

DESCRIPTION

GET

/api/v1/models/{model_id}/metrics/versions

Get metric versions for a model

POST

/api/v1/models/{model_id}/metrics/versions

Create a metric version

GET

/api/v1/models/{model_id}/alerts

Get model alerts

POST

/api/v1/models/{model_id}/alerts

Create model alerts

GET

/api/v1/models/{model_id}/alert_rules

Get model alert rules

GET

/api/v1/connectors/{connector_id}

Get a connector

GET

/api/v1/workspaces/{workspace_id}

Get a workspace

DELETE

/api/v1/workspaces/{workspace_id}

Delete a workspace

GET

/api/v1/models/{model_id}/metrics/versions

Get metric versions for a model

POST

/api/v1/models/{model_id}/metrics/versions

Create a metric version

GET

/api/v1/models/{model_id}/alerts

Get model alerts

POST

/api/v1/models/{model_id}/alerts

Create model alerts

GET

/api/v1/models/{model_id}/alert_rules

Get model alert rules

GET

/api/v1/connectors/{connector_id}

Get a connector

GET

/api/v1/workspaces/{workspace_id}

Get a workspace

DELETE

/api/v1/workspaces/{workspace_id}

Delete a workspace

Why Jentic?

What agents get from Jentic-routed access to this vendor.

Setup

Wiring Arthur Scope by hand means completing its OAuth2 authorization-code flow, pointing at the platform.arthur.ai host, and shaping model and metric requests yourself. Through Jentic you install once, import Arthur Scope from the Jentic API Directory, store the OAuth2 credentials once, and your agent calls it.

Permission scoping

Arthur Scope puts the model and workspace ids in the URL path (/api/v1/workspaces/{workspace_id}), so a rule can pin your agent to one workspace or model and nothing else. You choose which operations it may call, so destructive ones like deleting a workspace are not included unless you add them.

Credential isolation

Your Arthur OAuth2 credentials are stored once, encrypted, by your own Jentic One instance and injected at execution time. They never enter the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'get alerts for a model' or 'upload model metrics', and Jentic returns the matching Arthur Scope operation with its input schema so the agent calls the right endpoint without browsing the reference.

FAQs

Specific to using Arthur Scope API through Jentic.

What authentication does Arthur Scope use?

Arthur Scope uses OAuth2 with the authorization-code flow. Through Jentic, those credentials are stored encrypted in your Jentic One instance and injected at execution time, so the raw secrets never enter the agent context.

What can an agent do with Arthur Scope?

It can register models, publish metric versions and upload their values, query metrics over time, and manage model alerts and alert rules. Workspaces and connectors organise the models and their datasets. Responses are structured so an agent can act on them directly.

Can I set up model drift alerts through Arthur Scope?

Yes. The API lets an agent create alert rules on a model and read back the alerts that fire, so a drifting or degrading model can be flagged automatically.

What are the rate limits for Arthur Scope?

Rate limits are not specified in the OpenAPI spec. Check the Arthur documentation for current limits. Through Jentic, retries are handled in the execution layer.

How many endpoints does Arthur Scope have?

Arthur Scope exposes 202 endpoints covering models, metrics, alerts, workspaces, and connectors.

Can I limit what my agent is allowed to do with Arthur Scope?

Yes. Jentic One is self-hosted by you, so your own rules decide which Arthur operations your agent may call. Because Arthur puts the model and workspace ids in the URL path, such as /api/v1/workspaces/{workspace_id}, a rule can pin the agent to a single workspace and nothing else. You also choose which operations it may call, so destructive ones like DELETE /api/v1/workspaces/{workspace_id} to delete a workspace are excluded unless you add them.

Arthur Scope

AI ReadinessFoundational (C+)58/100

See full scorecard

Official vendor OpenAPI document · agent-readyDeveloper ToolsMonitoring Observabilityoauth2202 EndpointsREST

For Agents

Monitor production machine learning models with Arthur Scope: metrics, alerts, alert rules, workspaces, and connectors. Secured with OAuth2.

Use for: Monitor a machine learning model, Create an alert rule for model drift, Upload metrics for a model version, List the alerts for a model

Not supported: Does not handle model training or feature stores - use it to monitor and alert on production machine learning models only.

Arthur Scope is the monitoring and observability API for production machine learning. An agent can register models, publish metric versions and upload their values, query those metrics over time, and manage the alerts and alert rules that fire when a model drifts or degrades. Workspaces, connectors, and datasets organise the models and the data feeding them, so monitoring stays grouped by team and project.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: BSignals: 4

87%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

Specification Validity

Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).

75%

Structural Integrity

Structural correctness score based on schema issues using logarithmic dampening.

Developer Experience & Jentic Compatibility

Clarity, completeness, and ingestion readiness for developers and tooling.

Grade: B-Signals: 4

Example Density

How richly the API is illustrated with examples.

75%

Example Validity

Percentage of examples that conform to their schemas.

74%

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.

AI-Readiness & Agent Experience

Semantic breadth, depth, and agent comprehension for AI systems.

Grade: C-Signals: 4

50%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

100%

OperationId Quality

Coverage, uniqueness, and casing consistency of operationIds for AI inference.

61%

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: D-Signals: 1

43%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: A+Signals: 1

90%

Authentication Strength

Average quality of security schemes based on authentication method strength (weakest link for OAuth2).

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: B+Signals: 1

68%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Arthur Scope to your agent

Capabilities

Start building with Arthur Scope API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Arthur Scope

Jentic AI Readiness Score

Arthur Scope

Foundational Compliance

Lint Results

Resolution Completeness

Specification Validity

Structural Integrity

Developer Experience & Jentic Compatibility

Example Density

Example Validity

Response Coverage

Tooling Readiness

AI-Readiness & Agent Experience

Description Coverage

Error Standardization

OperationId Quality

Summary Coverage

Agent Usability

Complexity Comfort

Security

Authentication Strength

AI Discoverability

Descriptive Richness