Install Jentic One Beta

Connect the DecimalAI Platform to your agent

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

Ingest agent traces individually or in batches and run configurable evaluators against them

Promote annotated traces into versioned eval datasets and validate their quality for CI gates

Run regression checks for a candidate manifest against a baseline and raise or resolve regression alerts

Author, version, benchmark, and publish agent skills to a public registry

GET STARTED

Start building with DecimalAI Platform API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use DecimalAI Platform API for, with concrete tasks.

★ AI Agent Self-Observability

An AI agent connected to DecimalAI through Jentic can ingest its own run traces and immediately request evaluator scores on them, turning each run into measured feedback. It can list the evaluators configured for the agent, push external eval scores, and read a full eval breakdown with provenance, so the agent observes its own behaviour rather than running blind.

Ingest a trace for the current run, run the agent's configured evaluators on it, and read the eval breakdown with provenance

Regression Gating in CI

DecimalAI gates agent changes by running a regression check for a candidate manifest against the baseline and surfacing a verdict a CI pipeline can act on. Teams can validate a dataset version's quality for CI gates, record an engineer's human decision on a check, and roll back to a prior manifest in one call when a regression is confirmed.

Run a regression check for a candidate manifest against the baseline and, if it fails, roll back to the prior manifest

Eval Dataset Lifecycle

DecimalAI builds versioned eval datasets from production traces: annotated traces are promoted into a dataset, a new dataset version is built, and quality statistics and side-by-side version comparisons show whether the set is fit for evaluation. Exported versions and DPO preference pairs feed downstream fine-tuning and benchmarking.

Promote annotated traces into an eval dataset, build a new version, and read its quality statistics

Skill Registry and Benchmarking

The platform hosts a public skills registry where an agent's skills are authored, benchmarked, versioned, forked, and published. Benchmark runs produce per-case results, novelty checks flag near-duplicate skills before publishing, and verified-lift badges summarise a skill's measured effect, so skills are promoted on evidence rather than assertion.

Create a skill, run its benchmark suite on the latest version, and publish it to the registry once the results clear

Key Endpoints

333 endpoints — the decimalai platform api runs the evaluation and dataset lifecycle for ai agents: it ingests agent traces, scores them with configurable evaluators, and promotes annotated traces into versioned eval datasets.

METHOD

PATH

DESCRIPTION

POST

/api/v1/traces

Ingest a trace

POST

/api/v1/traces/{trace_id}/evaluate

Run evaluators on a trace

POST

/api/v1/datasets/{dataset_id}/build

Build a dataset version from traces

POST

/api/v1/regression-check

Run a regression check against the baseline

GET

/api/v1/evaluators

List evaluators configured for an agent

POST

/api/v1/skills

Create a skill

POST

/api/v1/training/jobs

Launch a fine-tuning job

GET

/api/v1/manifests/{manifest_id}/compatibility

Get compatibility reports for a manifest

POST

/api/v1/traces

Ingest a trace

POST

/api/v1/traces/{trace_id}/evaluate

Run evaluators on a trace

POST

/api/v1/datasets/{dataset_id}/build

Build a dataset version from traces

POST

/api/v1/regression-check

Run a regression check against the baseline

GET

/api/v1/evaluators

List evaluators configured for an agent

POST

/api/v1/skills

Create a skill

POST

/api/v1/training/jobs

Launch a fine-tuning job

GET

/api/v1/manifests/{manifest_id}/compatibility

Get compatibility reports for a manifest

Why Jentic?

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

Setup

Wiring DecimalAI by hand means handling its bearer API-key auth and its many surfaces: trace ingestion, evaluators, datasets, manifests, and regression checks. Through Jentic you install once, import DecimalAI from the API Directory, store the key once, and your agent calls it.

Permission scoping

DecimalAI is scoped per operation, and its destructive paths (cascade-deleting an agent, rolling back a manifest, removing org members, starting billing checkout) are separate from its read and ingest operations. A rule can let your agent ingest traces and read evaluations while those account-level operations stay out of its reach. You choose the operations it may call, so nothing destructive runs unless you add it.

Credential isolation

Your DecimalAI API 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 'ingest an agent trace' or 'run a regression check', and Jentic returns the matching DecimalAI operation with its input schema so the agent calls the right endpoint without browsing the reference docs.

FAQs

Specific to using DecimalAI Platform API through Jentic.

Is there a DecimalAI MCP server?

You don't need an MCP server to give your agent DecimalAI. Jentic connects it directly from the API Directory: import it, store your API key once, and your agent calls the trace, evaluator, dataset, and regression operations. Operations are discovered on demand, so no extra server's tool definitions sit in the agent's context.

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

Yes. Write a rule that allows trace ingestion and evaluator reads but not agent deletion, manifest rollback, or billing changes, so the agent can report and score its runs while account-level changes stay with you, and every call it makes is logged.

What authentication does the DecimalAI Platform use?

Per its OpenAPI spec, the DecimalAI Platform authenticates with a bearer API key in the dai_sk_ format, sent in the Authorization header; the whoami operation confirms the key's scope and permissions. Through Jentic the key is stored encrypted by your own Jentic One instance and injected only at call time.

Can I run regression checks for my agent with the DecimalAI Platform?

Yes. The regression-check operation runs a candidate manifest against the baseline and returns a verdict, you can record an engineer's human decision on the result, and a call-replay can test a model change behaviourally. Regression alerts can then be listed, acknowledged, and resolved from the on-call surface.

What are the rate limits for the DecimalAI Platform?

The OpenAPI spec does not specify request-rate limits; usage is instead governed by plan limits exposed through the billing plan and usage operations, which also project current-period usage. Check docs.decimal.ai for current plan limits and quotas.

How do I evaluate an agent trace with the DecimalAI Platform through Jentic?

Ingest the trace, then call the evaluate operation to run the agent's configured evaluators and read the resulting scores and breakdown. In Jentic, search for 'ingest and evaluate an agent trace', store your API key once, and the agent runs both steps. To run it on your own infrastructure, install Jentic One from its GitHub repo.

Decimal DecimalAI Platform

AI ReadinessFoundational (C-)53/100

See full scorecard

Official vendor OpenAPI document · agent-readyDeveloper ToolsMonitoring Observabilitybearer333 EndpointsREST

For Agents

Ingest AI agent traces, score them with evaluators, and build eval datasets. Runs regression checks, manages a skills registry, and launches fine-tuning jobs across 333 endpoints.

Use for: Ingest a trace from my agent run and run evaluators on it, Build an eval dataset from annotated traces, Run a regression check for a candidate manifest against the baseline, List the evaluators configured for an agent

Not supported: Does not run your agent in production, serve live end-user traffic, or manage hosting infrastructure. Use for tracing, evaluating, and regression-testing AI agents and their skills only.

The DecimalAI Platform API runs the evaluation and dataset lifecycle for AI agents: it ingests agent traces, scores them with configurable evaluators, and promotes annotated traces into versioned eval datasets. It gates changes with regression checks against a baseline manifest, raises and resolves regression alerts, and tracks per-agent cost and delegation analytics. It also hosts a public skills registry where an agent's skills are authored, benchmarked, versioned, and published, and it can launch fine-tuning jobs through configured providers.

Jentic One on Github View OpenAPI Document

Show dimension breakdown

Foundational Compliance

Base layer of spec validity and structural soundness.

Grade: C+Signals: 4

90%

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).

50%

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.

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.

AI-Readiness & Agent Experience

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

Grade: DSignals: 4

24%

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.

54%

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: 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: A-Signals: 1

75%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the DecimalAI Platform to your agent

Capabilities

Start building with DecimalAI Platform API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

Decimal DecimalAI Platform

Jentic AI Readiness Score

DecimalAI Platform

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