Amazon Augmented AI website screenshot

Amazon Augmented AI

Amazon Augmented AI (Amazon A2I) is a machine learning service that makes it easy to build the workflows required for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy lifting associated with building human review systems or managing large numbers of human reviewers.

Amazon Augmented AI publishes 1 API on the APIs.io network: Human Loops API. Tagged areas include Amazon Augmented AI, Human-in-the-Loop, Machine-Learning, and AI Review.

The Amazon Augmented AI catalog on APIs.io includes 1 JSON-LD context and 2 Spectral governance rulesets.

Amazon Augmented AI’s developer surface includes authentication and 8 more developer resources.

🌐 Visit website 📡 Source on GitHub
30.0/100 thin ▬ flat Agent 22/100 agent aware Full breakdown ↓
scored 2026-08-24 · rubric v0.13.0
AccessSelf serve
1 APIs 1 MCP Servers 10 Features 6 Use Cases
Amazon Augmented AIHuman-in-the-LoopMachine-LearningAI Review

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-08-24 · rubric v0.13.0
Composite quality — 30.0/100 · thin
Contract Quality 17.4 / 25
Developer Ergonomics 2.4 / 20
Access Clarity 0.0 / 20
Operational Transparency 0.0 / 13
Contract Governance 3.6 / 12
Discoverability 6.7 / 10
Agent readiness — 22/100 · agent aware
Machine-Readable Contract 18 / 18
Agentic Access Contract 10 / 10
Documented Reversibility 0 / 6
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 7 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3
A2A Agent Card 0 / 8
Dry-Run / Simulate Mode 0 / 4
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. This rating is computed from github.com/api-evangelist/amazon-augmented-ai: open an issue to ask a question, or submit a pull request to add artifacts. Want it done for you? Prioritized profiling — $2,500 →

APIs 1

Individual APIs this provider publishes, each with its own machine-readable definition.

Amazon Augmented AI Human Loops API

Operations for creating and managing human review loops

Open Collections 2

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

API Collection

OPEN COLLECTION

Amazon Augmented AI (A2I) Human Loops API

OPEN COLLECTION

MCP Servers 1

Model Context Protocol servers that expose these APIs to AI agents.

Amazon Augmented AI MCP Server

MCP SERVER

Features 10

Notable capabilities this provider offers.

Human review integration for Amazon Rekognition and Amazon Textract
Custom flow definitions for any ML use case
Built-in worker task templates for common review tasks
Integration with Amazon SageMaker Ground Truth for workforce management
Private, vendor, and Amazon Mechanical Turk workforce support
Automatic routing based on ML confidence scores
Audit trail with evidence of human review decisions
Scalable workforce management across thousands of reviewers
Pre-built UI templates for image and text review tasks
Compliance support with PII content classifiers

Scroll for all 10

Semantic Vocabularies 1

JSON-LD contexts and semantic vocabularies used across these APIs.

Amazon Augmented Ai Context

4 classes · 0 properties

JSON-LD

Spectral Rules 2

Spectral governance rulesets for linting and validating these APIs.

Amazon Augmented AI API Rules

4 rules · 3 warnings 1 info

SPECTRAL

Amazon Augmented AI API Rules

18 rules · 7 errors 11 warnings

SPECTRAL

JSON Schema 12

Standalone JSON Schema definitions for this provider's data models.

DataAttributes

0 properties

JSON SCHEMA

DeleteHumanLoopResponse

0 properties

JSON SCHEMA

DescribeHumanLoopResponse

0 properties

JSON SCHEMA

HumanLoopActivationResults

0 properties

JSON SCHEMA

HumanLoopInput

0 properties

JSON SCHEMA

HumanLoopOutput

0 properties

JSON SCHEMA

HumanLoopSummary

0 properties

JSON SCHEMA

ListHumanLoopsResponse

0 properties

JSON SCHEMA

StartHumanLoopRequest

0 properties

JSON SCHEMA

StartHumanLoopResponse

0 properties

JSON SCHEMA

StopHumanLoopRequest

0 properties

JSON SCHEMA

StopHumanLoopResponse

0 properties

JSON SCHEMA

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JSON Structure 12

JSON Structure definitions describing this provider's data shapes.

A2I Data Attributes Structure

0 properties

JSON STRUCTURE

A2I Delete Human Loop Response Structure

0 properties

JSON STRUCTURE

A2I Describe Human Loop Response Structure

0 properties

JSON STRUCTURE

A2I Human Loop Activation Results Structure

0 properties

JSON STRUCTURE

A2I Human Loop Input Structure

0 properties

JSON STRUCTURE

A2I Human Loop Output Structure

0 properties

JSON STRUCTURE

A2I Human Loop Summary Structure

0 properties

JSON STRUCTURE

A2I List Human Loops Response Structure

0 properties

JSON STRUCTURE

A2I Start Human Loop Request Structure

0 properties

JSON STRUCTURE

A2I Start Human Loop Response Structure

0 properties

JSON STRUCTURE

A2I Stop Human Loop Request Structure

0 properties

JSON STRUCTURE

A2I Stop Human Loop Response Structure

0 properties

JSON STRUCTURE

Scroll for all 12

Examples 12

Example request and response payloads for these APIs.

A2I Data Attributes Example

3 fields

EXAMPLE

A2I Delete Human Loop Response Example

3 fields

EXAMPLE

A2I Describe Human Loop Response Example

3 fields

EXAMPLE

A2I Human Loop Activation Results Example

3 fields

EXAMPLE

A2I Human Loop Input Example

3 fields

EXAMPLE

A2I Human Loop Output Example

3 fields

EXAMPLE

A2I Human Loop Summary Example

3 fields

EXAMPLE

A2I List Human Loops Response Example

3 fields

EXAMPLE

A2I Start Human Loop Request Example

3 fields

EXAMPLE

A2I Start Human Loop Response Example

3 fields

EXAMPLE

A2I Stop Human Loop Request Example

3 fields

EXAMPLE

A2I Stop Human Loop Response Example

3 fields

EXAMPLE

Scroll for all 12

Security Posture 3

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Amazon Augmented Ai Authentication

apiKey · 1 scheme

SECURITY

Amazon Augmented Ai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Amazon Augmented Ai Vulnerability Disclosure

Hackerone · security.txt · contact published

SECURITY

Agentic Access 1

Recommended x-agentic-access execution contracts for AI agents.

Amazon Augmented Ai Agentic Access

5 operations · 3 acting · 1 human-in-the-loop

5 operations · 3 acting

AGENTIC

Use Cases 6

What developers build with this provider.

Review low-confidence document text extraction results
Validate image classification predictions before deployment
Moderate user-generated content with human reviewers
Ensure accuracy of medical record processing
Verify identity document data extraction results
Build training datasets with human-verified labels

Integrations 10

Pre-built integrations with other platforms and tools.

Amazon SageMaker
Amazon Rekognition
Amazon Textract
Amazon S3
Amazon SageMaker Ground Truth
Amazon Mechanical Turk
AWS IAM
Amazon CloudWatch
AWS Lambda
Amazon SNS

Scroll for all 10

Resources

Agent Surfaces 4

MCP servers, agent skills, and machine-readable catalogs

MCPServer
MCPServer
 AgenticAccess
AgenticAccess
 LLMsTxt
LLMsTxt
 WellKnown
WellKnown

Build 1

SDKs, sample code, and the tooling you integrate with

Packages
Packages

Access & Security 3

Authentication, authorization, and security posture

VulnerabilityDisclosure
VulnerabilityDisclosure
 DomainSecurity
DomainSecurity
 Authentication
Authentication

Other 1

Properties that don't map to a standard resource type

Overlay
Overlay

Source (apis.yml)

apis.yml Raw ↑ 
aid: amazon-augmented-ai
name: Amazon Augmented AI
description: Amazon Augmented AI (Amazon A2I) is a machine learning service that makes it easy to build the workflows required
  for human review of ML predictions. Amazon A2I brings human review to all developers, removing the undifferentiated heavy
  lifting associated with building human review systems or managing large numbers of human reviewers.
type: Index
accessModel:
  pricing: unknown
  onboarding: self-serve
  trial: false
  try_now: false
  public: false
  label: Self-serve signup
  confidence: medium
  source:
  - authentication
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/amazon-augmented-ai.png
tags:
- Amazon Augmented AI
- Human-in-the-Loop
- Machine-Learning
- AI Review
- AWS
tags_raw:
- Amazon Augmented AI
- Human In The Loop
- Machine Learning
- AI Review
- AWS
url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/apis.yml
created: '2026-03-16'
modified: '2026-06-20'
specificationVersion: '0.23'
apis:
- aid: amazon-augmented-ai:amazon-augmented-ai-human-loops-api
  name: Amazon Augmented AI Human Loops API
  description: Operations for creating and managing human review loops
  humanURL: https://docs.aws.amazon.com/augmented-ai/2019-11-07/APIReference/Welcome.html
  baseURL: https://a2i-runtime.sagemaker.us-east-1.amazonaws.com
  tags:
  - Human Loops
  properties:
  - type: OpenAPI
    url: openapi/amazon-augmented-ai-human-loops-api-openapi.yml
  - type: SpectralRules
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/rules/amazon-augmented-ai-spectral-rules.yml
  - type: Vocabulary
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/vocabulary/amazon-augmented-ai-vocabulary.yaml
  - type: JSONSchema
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/json-schema/a2i-human-loop-summary-schema.json
  - type: JSONSchema
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/json-schema/a2i-start-human-loop-request-schema.json
  - type: JSONStructure
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/json-structure/a2i-human-loop-summary-structure.json
  - type: JSONStructure
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/json-structure/a2i-describe-human-loop-response-structure.json
  - type: JSONLD
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/json-ld/amazon-augmented-ai-context.jsonld
  - type: Example
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/examples/a2i-start-human-loop-request-example.json
  - type: Example
    url: https://raw.githubusercontent.com/api-evangelist/amazon-augmented-ai/refs/heads/main/examples/a2i-describe-human-loop-response-example.json
  - type: ErrorCatalog
    url: errors/amazon-augmented-ai-problem-types.yml
  - type: Conformance
    url: conformance/amazon-augmented-ai-conformance.yml
  - type: Lifecycle
    url: lifecycle/amazon-augmented-ai-lifecycle.yml
common:
- type: MCPServer
  url: mcp/amazon-augmented-ai-mcp.yml
- type: Overlay
  url: overlays/amazon-augmented-ai-openapi-overlay.yaml
- type: AgenticAccess
  url: agentic-access/amazon-augmented-ai-agentic-access.yml
- type: VulnerabilityDisclosure
  url: security/amazon-augmented-ai-vulnerability-disclosure.yml
- type: DomainSecurity
  url: security/amazon-augmented-ai-domain-security.yml
- type: Authentication
  url: authentication/amazon-augmented-ai-authentication.yml
- type: Packages
  url: packages/amazon-augmented-ai-packages.yml
- type: LLMsTxt
  url: llms/amazon-augmented-ai-llms.txt
- type: WellKnown
  url: well-known/amazon-augmented-ai-well-known.yml
- type: Features
  data:
  - Human review integration for Amazon Rekognition and Amazon Textract
  - Custom flow definitions for any ML use case
  - Built-in worker task templates for common review tasks
  - Integration with Amazon SageMaker Ground Truth for workforce management
  - Private, vendor, and Amazon Mechanical Turk workforce support
  - Automatic routing based on ML confidence scores
  - Audit trail with evidence of human review decisions
  - Scalable workforce management across thousands of reviewers
  - Pre-built UI templates for image and text review tasks
  - Compliance support with PII content classifiers
- type: UseCases
  data:
  - Review low-confidence document text extraction results
  - Validate image classification predictions before deployment
  - Moderate user-generated content with human reviewers
  - Ensure accuracy of medical record processing
  - Verify identity document data extraction results
  - Build training datasets with human-verified labels
- type: Integrations
  data:
  - Amazon SageMaker
  - Amazon Rekognition
  - Amazon Textract
  - Amazon S3
  - Amazon SageMaker Ground Truth
  - Amazon Mechanical Turk
  - AWS IAM
  - Amazon CloudWatch
  - AWS Lambda
  - Amazon SNS
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

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