Install Jentic One Beta

Connect the Daishi Studio API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Daishi Studio API, 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%2Fdaishi.ai%2Fdaishi" | 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%2Fdaishi.ai%2Fdaishi" | 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 Daishi Studio API.

Author and validate evaluation scenarios against the Studio schema

Estimate a run before launching it and then launch the run

Create experiments and read their reports

List and cancel runs, experiments, and series

Read usage and allowances, available models, and live worlds

GET STARTED

Start building with Daishi Studio API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Daishi Studio API for, with concrete tasks.

★ Agent-driven evaluation runs

An AI agent iterating on a design can validate a scenario, estimate the run, and launch it against a Daishi Studio account, then poll for the result. Because the Daishi Studio API estimates a run before it starts, the agent can confirm the cost is acceptable before committing to the evaluation.

Validate a scenario, estimate the run, launch it, then poll the run until it completes and read the report

Scenario authoring and validation

Building reliable evaluations starts with well-formed scenarios. The Daishi Studio API exposes the scenario schema and a validate operation, so an author can check a scenario before saving it and catch errors with the same validation the run builder uses. Library scenarios, anchor bots, and templates provide starting points.

Read the scenario schema, validate a draft scenario against it, then create the scenario once it passes

Experiment reporting

Comparing conditions needs structured results. The Daishi Studio API creates experiments, estimates them, and returns experiment reports and designs, so a workflow can launch a multi-condition experiment and read back the report for analysis. Experiments can be published so others can view the results.

Create an experiment, launch it, then read the experiment report once it finishes

Key Endpoints

48 endpoints — the daishi studio api authors evaluation scenarios and launches runs against them on a daishi studio account.

METHOD

PATH

DESCRIPTION

POST

/scenarios/validate

Validate a scenario against the Studio schema

POST

/scenarios

Create a scenario

POST

/runs/estimate

Estimate a run before launching

POST

/runs

Launch a run

GET

/runs/{id}

Get a run

POST

/experiments

Create an experiment

GET

/experiments/{id}

Get an experiment report

GET

/usage

Read usage and allowances

POST

/scenarios/validate

Validate a scenario against the Studio schema

POST

/scenarios

Create a scenario

POST

/runs/estimate

Estimate a run before launching

POST

/runs

Launch a run

GET

/runs/{id}

Get a run

POST

/experiments

Create an experiment

GET

/experiments/{id}

Get an experiment report

GET

/usage

Read usage and allowances

Why Jentic?

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

Setup

Wiring the Daishi Studio API by hand means handling its bearer-token auth, the validate-then-estimate-then-launch run flow, and the distinct scenario, run, and experiment shapes yourself. Through Jentic you install once, import Daishi Studio from the API Directory, store the token once, and your agent calls it.

Permission scoping

Runs and experiments are addressed by an id in the URL path, but a rule bounds which operations your agent may call rather than which record it touches. You choose the operations it may call, such as validating scenarios and launching runs, so cancelling a run or publishing a report is not included unless you add it.

Credential isolation

Your Daishi Studio token 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 'launch an evaluation run' or 'validate a scenario', and Jentic returns the matching Daishi Studio operation with its input schema so the agent calls the right endpoint without reading the reference docs.

FAQs

Specific to using Daishi Studio API through Jentic.

What authentication does the Daishi Studio API use?

The Daishi Studio API authenticates with a Studio access token sent as a bearer token, prefixed dsk_, as declared in its OpenAPI spec. The API index and the OpenAPI document need no token. Through Jentic the token is stored once and injected at call time, so it never appears in your agent's prompt or logs.

Can I estimate a run before launching it with the Daishi Studio API?

Yes. The API exposes estimate operations for both runs and experiments, so an agent can read the estimated cost before launching and only commit to the evaluation once the figure is acceptable.

What are the rate limits for the Daishi Studio API?

The OpenAPI spec exposes a usage and allowances endpoint but does not state numeric rate limits. Check the vendor documentation at https://daishi.ai for current limits and allowances before launching many runs.

How do I launch an evaluation run through Jentic with the Daishi Studio API?

Search Jentic for 'launch an evaluation run', which returns the POST /runs operation with its input schema, then validate the scenario and launch the run. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Can I limit what my agent is allowed to do with the Daishi Studio API?

Yes. Write a rule that allows only the scenario and run read-and-create operations, so the agent can author scenarios, launch runs, and read reports but cannot cancel a run or publish a report, and every call it makes is logged. You decide which operations are in the rule, so nothing destructive runs unless you add it.

Daishi Studio API

AI ReadinessAI-Aware (B-)62/100

See full scorecard

Official vendor OpenAPI document · agent-readyAI/MLMl Inferencebearer48 EndpointsREST

For Agents

Author and validate evaluation scenarios, estimate and launch runs and experiments, and read their reports and logs on a Daishi Studio account.

Use for: Launch an evaluation run for a scenario, Validate a scenario before saving it, Estimate the cost of a run, Get the report for an experiment

Not supported: Does not handle model training, production serving, or prompt storage. Use for authoring scenarios and running evaluations only.

The Daishi Studio API authors evaluation scenarios and launches runs against them on a Daishi Studio account. It validates a scenario against the Studio schema, estimates a run before launching, runs single evaluations and multi-condition experiments, and returns run logs, seats, and reports. Every write goes through the same validation as the Studio run builder, and runs, experiments, and series can be published or cancelled.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A-Signals: 4

94%

Lint Results

Aggregated quality score from linter diagnostics, weighted by severity.

100%

Resolution Completeness

Percentage of `$ref` references that resolve successfully.

100%

Specification Validity

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

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.

92%

Example Validity

Percentage of examples that conform to their schemas.

49%

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: DSignals: 4

46%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

75%

OperationId Quality

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

55%

Summary Coverage

Coverage of summaries across operations/tags/info.

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.

Security

Trust, risk posture, and security compliance.

Grade: B-Signals: 1

60%

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

69%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Daishi Studio API to your agent

Capabilities

Start building with Daishi Studio API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Daishi Studio API

Jentic AI Readiness Score

Daishi Studio API

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