Install Jentic One Beta

Connect the Amazon Comprehend to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Comprehend, 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%2Famazonaws.com%2Faws-comprehend" | 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%2Famazonaws.com%2Faws-comprehend" | 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 Amazon Comprehend API.

Detect entities, key phrases, dominant language, syntax, and sentiment in real time

Identify and redact personally identifiable information with ContainsPiiEntities and DetectPiiEntities

Run targeted sentiment analysis to attribute sentiment to specific entities

Train custom document classifiers and custom entity recognizers on labeled data

GET STARTED

Start building with Amazon Comprehend API

Explore with Jentic One

View OpenAPI Document

Use Cases

Patterns agents use Amazon Comprehend API for, with concrete tasks.

★ Customer Feedback Sentiment Analysis

Product teams need to understand whether customer reviews, support tickets, and survey responses are positive, negative, or mixed. Amazon Comprehend's DetectSentiment and BatchDetectSentiment return sentiment with confidence scores in real time, and StartSentimentDetectionJob handles archives of millions of documents asynchronously. A basic integration takes under a day.

Call BatchDetectSentiment with a list of 10 review texts and return the dominant sentiment and confidence scores per review.

PII Redaction in Customer Support

Compliance teams need to remove names, emails, phone numbers, and account IDs from logs and transcripts before they enter analytics or training pipelines. Amazon Comprehend's DetectPiiEntities and ContainsPiiEntities identify PII spans with type labels (EMAIL, SSN, PHONE, etc.), and StartPiiEntitiesDetectionJob redacts at scale across S3 inputs.

Call DetectPiiEntities on a customer transcript and return all spans with offsets, types, and confidence so the caller can mask them.

Custom Document Classification

Operations teams routing tickets, claims, or emails need a classifier tuned to their categories. Amazon Comprehend trains a custom classifier from labeled examples in S3, deploys it to a real-time endpoint, and serves ClassifyDocument calls. Training is asynchronous and typically completes in under an hour for small datasets.

Call CreateDocumentClassifier with an S3 path of labeled CSV training data, poll DescribeDocumentClassifier until the status is TRAINED, then create an endpoint and invoke ClassifyDocument.

Topic Modeling over a Document Corpus

Research and content teams need to discover themes across thousands of documents without labeled data. Amazon Comprehend's StartTopicsDetectionJob runs LDA-based topic modeling on a corpus in S3 and writes topic-term and document-topic distributions back to S3. Jobs typically run in a few hours depending on corpus size.

Start a topics detection job with an InputDataConfig pointing at an S3 prefix and 20 topics, then poll DescribeTopicsDetectionJob until status is COMPLETED.

Agent-Powered Text Triage

An AI agent triaging inbound communications can chain Comprehend operations: detect language, redact PII, classify the message, then extract entities. Through Jentic, the agent calls each operation by intent without managing SigV4 signing or boto3 sessions, and can deploy a custom classifier endpoint on demand.

For an input message: call DetectDominantLanguage, then DetectPiiEntities, then ClassifyDocument against a deployed endpoint, and return a structured triage result.

Key Endpoints

84 endpoints — jentic publishes the only available openapi specification for amazon comprehend, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/#X-Amz-Target=Comprehend_20171127.DetectSentiment

Detect sentiment in a single document

POST

/#X-Amz-Target=Comprehend_20171127.BatchDetectEntities

Detect entities in up to 25 documents

POST

/#X-Amz-Target=Comprehend_20171127.DetectPiiEntities

Detect PII spans in a document

POST

/#X-Amz-Target=Comprehend_20171127.ClassifyDocument

Classify a document using a custom classifier endpoint

POST

/#X-Amz-Target=Comprehend_20171127.CreateDocumentClassifier

Train a custom document classifier

POST

/#X-Amz-Target=Comprehend_20171127.StartTopicsDetectionJob

Run topic modeling over an S3 corpus

POST

/#X-Amz-Target=Comprehend_20171127.CreateEndpoint

Create a real-time inference endpoint

POST

/#X-Amz-Target=Comprehend_20171127.DetectSentiment

Detect sentiment in a single document

POST

/#X-Amz-Target=Comprehend_20171127.BatchDetectEntities

Detect entities in up to 25 documents

POST

/#X-Amz-Target=Comprehend_20171127.DetectPiiEntities

Detect PII spans in a document

POST

/#X-Amz-Target=Comprehend_20171127.ClassifyDocument

Classify a document using a custom classifier endpoint

POST

/#X-Amz-Target=Comprehend_20171127.CreateDocumentClassifier

Train a custom document classifier

POST

/#X-Amz-Target=Comprehend_20171127.StartTopicsDetectionJob

Run topic modeling over an S3 corpus

POST

/#X-Amz-Target=Comprehend_20171127.CreateEndpoint

Create a real-time inference endpoint

Why Jentic?

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

Setup

Wiring Amazon Comprehend by hand means signing every request with AWS Signature v4 HMAC, targeting the right regional host like comprehend.{region}.amazonaws.com, and dispatching each operation through the X-Amz-Target header instead of a REST path. Through Jentic you install once, import Amazon Comprehend from the API Directory, store the AWS access key and secret once, and your agent calls it.

Permission scoping

Comprehend routes operations through the X-Amz-Target header and carries the input text and job configuration in the request body, not the URL path, so scoping is by operation. You limit the agent to the operations it needs, such as DetectSentiment or DetectPiiEntities, so training and cost operations like CreateDocumentClassifier or CreateEndpoint are not included unless you add them.

Credential isolation

Your AWS access key and secret are stored once, encrypted, by your own Jentic One instance and injected at execution time when the request is signed. They never enter the agent's prompt, logs, or context.

Intent-based discovery

Agents search Jentic by intent such as 'detect entities and sentiment in text' or 'redact PII from a document', and Jentic returns the matching Comprehend operation with its input schema, so the agent calls DetectSentiment or DetectPiiEntities without browsing the AWS service reference.

FAQs

Specific to using Amazon Comprehend API through Jentic.

Why is there no official OpenAPI spec for Amazon Comprehend?

AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Comprehend via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.

What authentication does the Amazon Comprehend API use?

AWS Signature v4 (HMAC) signing with an IAM principal that has comprehend:* permissions. Jentic stores the AWS credentials in its vault and signs each request, so the agent only sees the operation result.

Can I redact PII automatically with the Amazon Comprehend API?

Yes. Use DetectPiiEntities to get spans, types, and offsets for each PII mention, or StartPiiEntitiesDetectionJob with Mode=ONLY_REDACTION to write redacted documents back to S3 in bulk.

What languages does the Amazon Comprehend API support?

DetectDominantLanguage covers 100+ languages. Sentiment, key phrases, entities, and syntax support a smaller set including English, Spanish, French, German, Italian, Portuguese, Japanese, Korean, Hindi, Arabic, and simplified Chinese - check the spec's LanguageCode enum for each operation.

What are the rate limits for the Amazon Comprehend API?

Per-account, per-region TPS limits apply per operation; Detect* synchronous calls are higher TPS than custom classifier endpoints, which are limited by provisioned inference units. Plan for ThrottlingException with exponential backoff or use BatchDetect* and async jobs for high volume.

How do I run sentiment analysis with the Amazon Comprehend API through Jentic?

Search Jentic for 'detect the sentiment of a piece of text', load the DetectSentiment schema, and execute it with Text and LanguageCode. For large batches, use BatchDetectSentiment (up to 25 documents per call) or StartSentimentDetectionJob for S3 inputs.

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

Yes. Because Amazon Comprehend routes every operation through the X-Amz-Target header rather than a URL path, your self-hosted Jentic One instance scopes access by operation, and your own rules decide which ones the agent may call. You can allow read-style analysis operations like DetectSentiment and DetectPiiEntities while excluding training and cost operations such as CreateDocumentClassifier and CreateEndpoint. The AWS access key and secret are injected only at execution time under those rules, so the agent can invoke only the Comprehend operations you have permitted.

AWS Amazon Comprehend

Name: Amazon Comprehend API
Brand: Amazon Comprehend
Availability: InStock

Browse all Amazonaws APIs

AI ReadinessAI-Aware (B-)60/100

See full scorecard

Agent-ready OpenAPI document · curated by JenticAI/MLLanguage ModelsAWS Signature v4 (HMAC)84 EndpointsREST

Know of an official OpenAPI document? Contribute it →

For Agents

Run NLP on text and documents - detect entities, sentiment, language, PII, and key phrases, plus train and run custom classifiers and entity recognizers.

Use for: I need to detect the sentiment of a customer review, I want to redact PII from support tickets before storing them, Find all named entities in a batch of news articles, Train a custom classifier to label support tickets by category

Not supported: Does not handle OCR, machine translation, speech transcription, or general LLM chat - use Amazon Comprehend for NLP analysis of already-extracted text only.

Jentic publishes the only available OpenAPI specification for Amazon Comprehend, keeping it validated and agent-ready. Amazon Comprehend is a managed natural language processing service that extracts insights from unstructured text. It detects entities, key phrases, sentiment, targeted sentiment, dominant language, syntax, and personally identifiable information, and supports custom classification and entity recognition models trained on your data. Both real-time analysis and large-scale asynchronous batch jobs are supported, along with topic modeling, document classification, and PII redaction.

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).

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.

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: FSignals: 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.

Summary Coverage

Coverage of summaries across operations/tags/info.

Agent Usability

Functional utility, complexity comfort, and AI orchestration readiness.

Grade: ASignals: 1

88%

Complexity Comfort

Agent comfort level based on API operational and structural complexity.

Security

Trust, risk posture, and security compliance.

Grade: C-Signals: 1

50%

Authentication Strength

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

100

AI Discoverability

Findability, semantic richness, and reasoning readiness.

Grade: A+Signals: 1

100%

Descriptive Richness

Clarity and depth of descriptions across API elements.

Connect the Amazon Comprehend to your agent

Capabilities

Start building with Amazon Comprehend API

Use Cases

Key Endpoints

Why Jentic?

Related APIs

FAQs

AWS Amazon Comprehend

Jentic AI Readiness Score

Amazon Comprehend

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