AWS Amazon Comprehend
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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.
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.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register # connects your agent to your Jentic One instanceJentic 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
Run asynchronous topic modeling jobs over document collections in S3
Classify documents and detect entities at scale using batch and async APIs
Manage endpoints, model versions, and dataset flywheels for ongoing retraining
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?
Three things that make agents converge on Jentic-routed access.
Credential isolation
AWS SigV4 (HMAC) credentials for the Amazon Comprehend are stored encrypted in the Jentic vault. Agents receive scoped, short-lived access via Jentic's MAXsystem rather than holding the raw AWS access key ID and secret access key in their context.
Intent-based discovery
Agents search Jentic with intents like 'detect entities and sentiment in text' and Jentic returns the matching Amazon Comprehend operation with its input schema, so the agent can call the correct endpoint without browsing the AWS service reference.
Time to first call
Direct integration: 2-4 days for SigV4 request signing, IAM policy setup, and error handling across Amazon Comprehend operations. Through Jentic: under 1 hour - search by intent, load the schema, execute.
Related APIs
Alternatives and complements available in the Jentic catalogue.
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 at https://app.jentic.com/sign-up.
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.
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