Install Jentic One Beta

Connect the Future AGI API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Future AGI 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%2Ffutureagi.com%2Ffutureagi" | 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%2Ffutureagi.com%2Ffutureagi" | 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 Future AGI API.

Create and apply evaluation groups that score your AI application's traced outputs

Record evaluation scores against a source and read them back by source

Bulk-annotate spans pulled from your traces for later review

Route items through annotation queues, assigning and submitting human annotations

Track inter-annotator agreement and queue progress for a review project

GET STARTED

Start building with Future AGI API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Future AGI API for, with concrete tasks.

★ Agent-Scored Output Evaluation

An AI agent connected through Jentic creates an evaluation group, applies it to a batch of traced outputs, and reads back the resulting scores, so quality measurement runs as a step in the agent's own workflow rather than a separate dashboard task. Because scores are stored against their source, the agent can later pull the scores for that source to decide whether an output passed.

Create an evaluation group, apply it to the latest traced outputs, and return the resulting scores for that source

Human-in-the-Loop Annotation

A review team builds an annotation queue, adds items to it, and assigns them to annotators who submit labels on each item, while the API tracks queue progress and inter-annotator agreement. This turns ad hoc labeling into a managed review project with a clear record of who annotated what and how much reviewers agreed.

Create an annotation queue, add these items, assign them to a reviewer, and report the queue progress

Agent Simulation and Regression Testing

A developer generates a test scenario for a saved agent definition, then creates and executes a test run to check the agent against that scenario before a release. Running tests through the API means regression checks can sit inside a CI step rather than a manual review, with each run tied to a specific agent definition version.

Generate a scenario for this agent definition, create a test run, and execute it

Key Endpoints

56 endpoints — the future agi api evaluates, annotates, and tests the outputs of ai applications so a team can measure quality and catch regressions before they ship.

METHOD

PATH

DESCRIPTION

POST

/model-hub/eval-groups/

Create an evaluation group

GET

/model-hub/eval-groups/

List evaluation groups

POST

/model-hub/eval-groups/apply-eval-group/

Apply an evaluation group to outputs

POST

/model-hub/scores/

Create an evaluation score

POST

/tracer/bulk-annotation/

Bulk-annotate trace spans

POST

/model-hub/annotation-queues/

Create an annotation queue

POST

/simulate/run-tests/create/

Create a test run

POST

/simulate/run-tests/{run_test_id}/execute/

Execute a test run

POST

/model-hub/eval-groups/

Create an evaluation group

GET

/model-hub/eval-groups/

List evaluation groups

POST

/model-hub/eval-groups/apply-eval-group/

Apply an evaluation group to outputs

POST

/model-hub/scores/

Create an evaluation score

POST

/tracer/bulk-annotation/

Bulk-annotate trace spans

POST

/model-hub/annotation-queues/

Create an annotation queue

POST

/simulate/run-tests/create/

Create a test run

POST

/simulate/run-tests/{run_test_id}/execute/

Execute a test run

Why Jentic?

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

Setup

Wiring the Future AGI API by hand means handling its bearer-token auth, learning the model-hub and tracer route groups, and threading evaluation groups, scores, and annotation queues together yourself. Through Jentic you install once, import Future AGI from the API Directory, store the token once, and your agent calls it.

Permission scoping

Future AGI carries most ids in the URL path, such as a score id or an annotation queue id, so a rule can bound your agent to the operations it needs. You choose which operations it may call, so destructive ones like deleting a score or a queue are not included unless you add them.

Credential isolation

Your Future AGI 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 'score my AI outputs' or 'annotate trace spans', and Jentic returns the matching Future AGI operation with its input schema so the agent calls the right endpoint without reading the reference docs.

FAQs

Specific to using Future AGI API through Jentic.

Is there a Future AGI MCP server?

You don't need an MCP server to give your agent the Future AGI API. Jentic connects it directly from the API Directory: import it, store your credential once, and your agent can create evaluation groups, record scores, and run agent tests straight away, with no extra server to run and no tool definitions to load into the agent's context.

Can I limit what my agent is allowed to do with the Future AGI API?

Yes. Write a rule that allows only the operations the agent needs, such as creating scores and reading evaluation logs, so it can measure output quality without touching review queues or labels. Destructive operations like deleting a score or a queue are not included unless you add them, and every call the agent makes is logged.

What authentication does the Future AGI API use?

The Future AGI API authenticates with a bearer token, where your API key is sent as a Bearer token in the Authorization header, per its OpenAPI spec. Through Jentic the token is stored encrypted by your own instance and added to each request at call time, so it never appears in the agent's prompt or logs.

Can I score my AI application's outputs with the Future AGI API?

Yes. You create an evaluation group, apply it to traced outputs, and the API records a score against each source, which you can then read back per source or list in bulk. This lets an agent or a CI step measure output quality without a human opening a dashboard.

What are the rate limits for the Future AGI API?

The OpenAPI spec does not specify rate limits for the Future AGI API. For current limits and fair-use guidance, see the Future AGI documentation at https://docs.futureagi.com.

How do I run an agent evaluation through Jentic?

Import the Future AGI API from the Jentic API Directory, then have your agent issue a request such as 'create an evaluation group and apply it to my traced outputs'. Jentic matches the intent to the create and apply operations and returns their input schema so the agent builds the correct request, with your stored credential injected at call time. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Future AGI API

AI ReadinessAI-Aware (B)67/100

See full scorecard

Official vendor OpenAPI document · agent-readyDeveloper ToolsMonitoring Observabilitybearer56 EndpointsREST

For Agents

Run evaluation groups and record scores on your AI application's traced outputs, manage human annotation queues, and execute agent test runs in Future AGI. Authenticates with a bearer token.

Use for: Create an evaluation group and apply it to my traced outputs, Record evaluation scores for a source and read them back, Bulk-annotate the spans from a recent trace, Set up an annotation queue and assign items to reviewers

Not supported: Does not host language models, serve production inference, or manage billing. Use for evaluating, annotating, and testing AI application outputs only.

The Future AGI API evaluates, annotates, and tests the outputs of AI applications so a team can measure quality and catch regressions before they ship. It runs evaluation groups that score traced outputs, records those scores against a source, and bulk-annotates spans pulled from your traces. It also drives human review through annotation queues, where items are assigned, annotated, and tracked for inter-annotator agreement, and it lets you generate test scenarios and execute test runs against a saved agent definition.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: BSignals: 4

60%

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).

100%

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: A-Signals: 4

81%

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

25%

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

55%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

98%

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

93%

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

70%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Future AGI API to your agent

Capabilities

Start building with Future AGI API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Future AGI API

Jentic AI Readiness Score

Future AGI 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