Install Jentic One Beta

Connect the Human4AI API to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Human4AI 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%2Fhuman4ai.ai%2Fhuman4ai" | 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%2Fhuman4ai.ai%2Fhuman4ai" | 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 Human4AI API.

Record a correction with the context, original reply, corrected reply, reason, and category, attributed to the agent key that sent it

Draft an unverified experience from a correction with a condition, action, and boundary

Edit a draft experience with an optimistic revision check, which invalidates earlier test evidence

Log positive and boundary test observations with expected and actual output and a pass or fail verdict

GET STARTED

Start building with Human4AI API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Human4AI API for, with concrete tasks.

★ Support agent that learns from corrections through Jentic

A developer's support agent logs every reply a human corrects as a Human4AI correction, then drafts a candidate experience from it, both through Jentic. The agent key is injected by the developer's own Jentic One instance, and activation stays with the workspace owner's browser session, so the agent can propose and test knowledge without being able to switch it on.

Call POST /api/v1/cases with title 'Refund window wording', context, original, corrected, reason, and category refunds, then call POST /api/v1/rules with the returned correctionId, a title, a condition, an action, and a boundary

Test an experience before owner review

Before an experience is activated, collect evidence that it behaves as intended. The agent records a positive test and a boundary test against the current revision, each with input, expected, actual, a pass or fail verdict, and a note. The owner-only activation call requires current human positive and boundary passes and no failed evidence, so agent evidence informs the decision without replacing it.

Call GET /api/v1/rules/{id} to read the current revision, then POST /api/v1/rules/{id}/tests twice with that revision: once with kind positive and once with kind boundary, each with input, expected, actual, verdict, and note

Load active support knowledge into an agent

Refresh a support agent's working knowledge from what the team has approved. The export call returns the workspace's active experiences, up to 1000 rules, and the spec notes that the export does not grant any external action permissions, so the content is reference material for the agent, not instructions to act on.

Call GET /api/v1/export and load each returned experience's condition, action, and boundary into the support agent's reference notes

Learn from public synthetic cases

Browse the anonymous public directory of synthetic support cases to see how other corrections were turned into experiences. The public case search matches Chinese or English titles and summaries, each case has a feedback thread, and a feedback or write key can submit discussion, test, or reuse feedback that starts as pending until the owner reviews it.

Call GET /api/public/cases with q refund, open the first result with GET /api/public/cases/{slug}, then submit POST /api/v1/feedback with that caseSlug, kind test, a message, and an outcome

Key Endpoints

28 endpoints — the human4ai api records the corrections a support team makes to ai-drafted customer replies and turns them into tested, scoped experiences that a support agent can reuse.

METHOD

PATH

DESCRIPTION

POST

/api/v1/cases

Record a correction

GET

/api/v1/cases

List corrections

POST

/api/v1/rules

Create an unverified candidate experience

PATCH

/api/v1/rules/{id}

Edit a draft with a revision check

POST

/api/v1/rules/{id}/tests

Record test observations for a revision

GET

/api/v1/export

Export active knowledge

POST

/api/v1/rules/{id}/activate

Owner-only activation

GET

/api/public/cases

Search the public synthetic case directory

POST

/api/v1/cases

Record a correction

GET

/api/v1/cases

List corrections

POST

/api/v1/rules

Create an unverified candidate experience

PATCH

/api/v1/rules/{id}

Edit a draft with a revision check

POST

/api/v1/rules/{id}/tests

Record test observations for a revision

GET

/api/v1/export

Export active knowledge

POST

/api/v1/rules/{id}/activate

Owner-only activation

GET

/api/public/cases

Search the public synthetic case directory

Why Jentic?

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

Setup

Wiring the Human4AI API by hand means keeping two credentials apart, an Agent bearer key for agent calls and the owner session cookie for activation and key management, sending an Idempotency-Key on writes, and following opaque list cursors. Through Jentic you install once, import Human4AI from the API Directory, store the Agent key once, and your agent calls it.

Permission scoping

Human4AI puts the experience id in the URL path (/api/v1/rules/{id}/...), so a rule can pin your agent to testing or editing one experience. You choose the operations it may call, so feedback submission and draft edits are not included unless you add them.

Credential isolation

Your Human4AI Agent key 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 'record a reply correction' or 'export support knowledge', and Jentic returns the matching Human4AI operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using Human4AI API through Jentic.

What authentication does the Human4AI API use?

The spec declares two schemes. Agent calls use a bearer token, an owner-issued Agent key in the form h4a_agent_ followed by random characters, with read, write, or feedback scope. Owner-only operations such as activating an experience, publishing feedback, and managing keys use the owner browser session, an apiKey-type cookie named `__Host-h4a`. The health check, workspace creation, recovery-key restore, and the public case endpoints need no credential. Through Jentic you store the Agent key, encrypted by your own Jentic One instance; the spec says never to give recovery keys to agents.

Can my agent activate an experience with the Human4AI API?

No. POST /api/v1/rules/{id}/activate is marked owner only and needs the owner session, and the spec states that no Agent key permits publication, activation, or key management. An agent can create candidates, edit drafts, and record test evidence; activation also requires current human positive and boundary passes and no failed evidence.

What are the rate limits for the Human4AI API?

The spec states a limit of 60 writes per workspace per minute and a payload limit of 32KB. It does not state a read limit; check https://human4ai.ai for current details.

How do I record a correction with the Human4AI API through Jentic?

Search Jentic for 'record a support reply correction' to get POST /api/v1/cases, then send the required title, context, original, corrected, reason, and category, with an Idempotency-Key header so a retry does not create a duplicate. To run it on your own infrastructure, install Jentic One from its GitHub repo and store your Agent key once.

Is there a Human4AI MCP server?

You don't need an MCP server to give your agent Human4AI. Jentic connects it directly from the API Directory: import it, store your Agent key once, and your agent calls the operations you allow, discovered on demand rather than loaded into its context up front.

Can I limit what my agent is allowed to do with the Human4AI API?

Yes. For example, allow only POST /api/v1/cases and POST /api/v1/rules/{id}/tests, so the agent can log corrections and record test evidence but cannot edit experience drafts or read the export. Human4AI's own key scopes add a second layer: the spec says a feedback-scope key only reaches public feedback operations, never private cases. Every call the agent makes is logged by your Jentic One instance.

Human4AI API

AI ReadinessAI-Aware (B-)62/100

See full scorecard

Official vendor OpenAPI document · agent-readyCRMCustomer Supportbearer, apiKey28 EndpointsREST

For Agents

Record corrections to AI support replies, draft experiences with a condition, action, and boundary, log test evidence against a revision, and export the workspace's active support knowledge.

Use for: Record that my refund reply was corrected and why, Draft an experience from correction c_123 for late-delivery tickets, Log a failed boundary test against revision 2 of this experience, Export the active support experiences for my agent

Not supported: Does not send customer replies, manage support tickets or inboxes, or run the support agent itself. Use for recording reply corrections and curating tested support experiences only.

The Human4AI API records the corrections a support team makes to AI-drafted customer replies and turns them into tested, scoped experiences that a support agent can reuse. Each correction stores the context, the original reply, the corrected reply, the reason, and a category; an experience built from it carries a condition, an action, and a boundary, and collects positive and boundary test evidence against a specific revision. Only the workspace owner can activate or archive an experience, and the export call returns active knowledge (up to 1000 rules) for loading into a support agent. A separate public area lists synthetic example cases and owner-published feedback on them.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: A+Signals: 4

96%

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).

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: B+Signals: 4

Example Density

How richly the API is illustrated with examples.

100%

Example Validity

Percentage of examples that conform to their schemas.

75%

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: D-Signals: 4

17%

Description Coverage

Coverage of descriptions across API elements.

Error Standardization

Coverage of RFC 9457 Problem Details for error responses.

93%

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: CSignals: 1

55%

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: CSignals: 1

55%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Human4AI API to your agent

Capabilities

Start building with Human4AI API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Human4AI API

Jentic AI Readiness Score

Human4AI 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