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.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Amazon Augmented AI the way a machine reads it — 72 machine-readable artifacts across 1 API, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Amazon Augmented AI scores 30.4/100 (thin), with a separate agent-readiness read of 25/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance. Every facet and dimension name is a link: it opens that measurement's page on APIs.io, where the rating runs across the whole catalog — the exact checks that feed it, how every profiled provider distributes on it, and who is at the top of it.
Kin Score
How this is scored →scored 2026-09-10 · rubric v0.20.0
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How we profile Amazon Augmented AI
Each block below is one kind of artifact we hold for Amazon Augmented AI. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 1
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
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 collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
API Collection
OPEN COLLECTIONAmazon Augmented AI (A2I) Human Loops API
OPEN COLLECTIONFeatures 10
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Scroll within the panel for all 10 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Amazon Augmented Ai Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon Augmented AI API Rules
SPECTRALAmazon Augmented AI API Rules
SPECTRALJSON Schema 12
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
DataAttributes
JSON SCHEMADeleteHumanLoopResponse
JSON SCHEMADescribeHumanLoopResponse
JSON SCHEMAHumanLoopActivationResults
JSON SCHEMAHumanLoopInput
JSON SCHEMAHumanLoopOutput
JSON SCHEMAHumanLoopSummary
JSON SCHEMAListHumanLoopsResponse
JSON SCHEMAStartHumanLoopRequest
JSON SCHEMAStartHumanLoopResponse
JSON SCHEMAStopHumanLoopRequest
JSON SCHEMAStopHumanLoopResponse
JSON SCHEMAScroll within the panel for all 12 ·
JSON Structure 12
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
A2I Data Attributes Structure
JSON STRUCTUREA2I Delete Human Loop Response Structure
JSON STRUCTUREA2I Describe Human Loop Response Structure
JSON STRUCTUREA2I Human Loop Activation Results Structure
JSON STRUCTUREA2I Human Loop Input Structure
JSON STRUCTUREA2I Human Loop Output Structure
JSON STRUCTUREA2I Human Loop Summary Structure
JSON STRUCTUREA2I List Human Loops Response Structure
JSON STRUCTUREA2I Start Human Loop Request Structure
JSON STRUCTUREA2I Start Human Loop Response Structure
JSON STRUCTUREA2I Stop Human Loop Request Structure
JSON STRUCTUREA2I Stop Human Loop Response Structure
JSON STRUCTUREScroll within the panel for all 12 ·
Examples 12
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
A2I Data Attributes Example
EXAMPLEA2I Human Loop Input Example
EXAMPLEScroll within the panel for all 12 ·
Security Posture 3
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 6
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Integrations 10
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Scroll within the panel for all 10 ·
Resources
Every other property we hold for Amazon Augmented AI — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Agent Surfaces 4
MCP servers, agent skills, and machine-readable catalogs
Build 1
SDKs, sample code, and the tooling you integrate with
Access & Security 3
Authentication, authorization, and security posture
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/amazon-augmented-ai · machine-readable index on apis.io
This is an independent, third-party profile of Amazon Augmented AI, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.
The Kin Score and Agent Readiness rating are independently calculated assessments of a company's public API artifacts, scored against a published rubric. They are not certifications, endorsements, security assessments, or audits.
Corrections, re-scores, and removal are free — no partnership or purchase required, and you do not need to justify the request. A removed company is recorded as unrated, never scored zero for having asked. Acknowledgement within one business day; removal within two.
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