AWS Amazon Augmented AI Runtime
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For Agents
Send low-confidence ML predictions to human reviewers and retrieve the human-validated result, so agents can defer hard cases instead of guessing.
Use for: Send a low-confidence model prediction to a human reviewer, Check whether a human review task has completed and retrieve the verdict, List all pending human review tasks for a flow definition, Stop a human review task that is no longer needed
Not supported: Does not create flow definitions, manage workforces, or run inference itself — use for starting, reading, stopping, and deleting individual human review tasks against an existing flow definition only.
Jentic publishes the only available OpenAPI specification for Amazon Augmented AI Runtime, keeping it validated and agent-ready. Amazon Augmented AI (A2I) is the human-in-the-loop layer for ML predictions — when a model's confidence falls below threshold, A2I routes the prediction to a configured workforce (private, vendor, or Amazon Mechanical Turk) for human review. The runtime API starts, monitors, stops, and deletes individual human review tasks (called HumanLoops) tied to a flow definition you create in the SageMaker console.
Install Jentic One Beta
Connect the Amazon Augmented AI Runtime to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Augmented AI Runtime, 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.
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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 Augmented AI Runtime API.
Start a human review task with StartHumanLoop attached to a flow definition and input data
Read the status and reviewer output of a human review task with DescribeHumanLoop
List human review tasks created against a given flow definition with ListHumanLoops
Stop an in-progress human review task with StopHumanLoop when the result is no longer needed
Delete a completed human review task with DeleteHumanLoop to clean up state
Pass arbitrary structured input to a human review task and receive structured reviewer output
Use Cases
Patterns agents use Amazon Augmented AI Runtime API for, with concrete tasks.
★ Human-in-the-loop for low-confidence predictions
ML teams use A2I to defer borderline predictions to human reviewers instead of acting on them blindly. When a SageMaker endpoint returns a confidence score below threshold, the application calls StartHumanLoop with the input data and the FlowDefinitionArn that defines the reviewer UI and workforce; once a reviewer submits, DescribeHumanLoop returns the structured human-validated result.
Call StartHumanLoop with FlowDefinitionArn for the document-classification flow, HumanLoopName='doc-1234', and the document JSON as input, then poll DescribeHumanLoop until status is Completed.
Compliance and audit review of ML decisions
Regulated teams use A2I to ensure a percentage of automated decisions are reviewed by qualified humans for audit. By configuring the flow definition to sample a fixed proportion of predictions and route them to a private workforce, A2I produces a defensible record of human-validated outcomes that ListHumanLoops and DescribeHumanLoop can replay during audits.
Call ListHumanLoops with FlowDefinitionArn and a CreationTimeAfter filter to retrieve all sampled review tasks from the last quarter for audit export.
Document and image review for ML pipelines
Document- and image-processing teams use A2I to add human verification to OCR, object-detection, and form-extraction pipelines. The flow definition renders the document or image alongside the model's prediction in the reviewer UI; the reviewer corrects or confirms it; DescribeHumanLoop returns the corrected result for downstream pipeline steps.
Submit StartHumanLoop with the Textract output as InputContent and the document-extraction flow definition, then on Completed status feed reviewer-corrected fields back into the downstream pipeline.
Agent-driven human-fallback through Jentic
AI agents that need to defer hard cases use Jentic to call StartHumanLoop and DescribeHumanLoop without holding raw AWS credentials. Jentic stores keys in the MAXsystem vault, signs each request with SigV4, and returns the structured response — so an agent can pause its workflow until a human verdict arrives, then resume with the validated result.
Search Jentic for 'send a prediction for human review', load StartHumanLoop, and execute it when the agent's own confidence is below 0.7, then poll DescribeHumanLoop until Completed.
Key Endpoints
5 endpoints — jentic publishes the only available openapi specification for amazon augmented ai runtime, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/human-loops
Start a human review task
GET
/human-loops/{HumanLoopName}
Read the status and output of a human review task
GET
/human-loops
List human review tasks for a flow definition
POST
/human-loops/stop
Stop an in-progress human review task
DELETE
/human-loops/{HumanLoopName}
Delete a completed human review task
POST
/human-loops
Start a human review task
GET
/human-loops/{HumanLoopName}
Read the status and output of a human review task
GET
/human-loops
List human review tasks for a flow definition
POST
/human-loops/stop
Stop an in-progress human review task
DELETE
/human-loops/{HumanLoopName}
Delete a completed human review task
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
AWS credentials are stored encrypted in the Jentic vault (MAXsystem). Each A2I Runtime call is signed with SigV4 server-side; the agent only receives scoped, short-lived authorisation, so raw access keys never enter the agent's context.
Intent-based discovery
Agents search by intent (e.g., 'send a prediction for human review') and Jentic returns matching A2I operations with their input schemas, so the agent calls StartHumanLoop or DescribeHumanLoop without browsing AWS docs.
Time to first call
Direct A2I Runtime integration: 1 day for SigV4 signing and S3-output handling. Through Jentic: under 30 minutes for the API surface — search, load schema, execute (the flow definition still needs to exist in SageMaker).
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Amazon Augmented AI Runtime API through Jentic.
Why is there no official OpenAPI spec for Amazon Augmented AI Runtime?
AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Augmented AI Runtime 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 Augmented AI Runtime API use?
A2I Runtime uses AWS Signature Version 4 HMAC signing with an access key ID and secret access key, optionally with a session token. Through Jentic, those credentials live in the MAXsystem vault and the agent never handles raw secrets — Jentic signs each request server-side.
Can I create a flow definition with the Amazon Augmented AI Runtime API?
No — flow definitions are created via the SageMaker control-plane API (CreateFlowDefinition), not the A2I runtime. The runtime only starts, reads, stops, and deletes individual HumanLoop tasks against an already-defined flow. Set up the flow definition first in SageMaker, then call StartHumanLoop here.
What are the rate limits for the Amazon Augmented AI Runtime API?
AWS does not publish a fixed RPS for A2I Runtime in the spec; standard AWS API throttling applies. The deeper bottleneck is reviewer throughput, not API rate — high-volume workflows should size the workforce appropriately and use ListHumanLoops to monitor backlog.
How do I send a model prediction for human review through Jentic?
Search Jentic for 'send a prediction for human review', load StartHumanLoop, and call POST /human-loops with FlowDefinitionArn, a unique HumanLoopName, and the input JSON. Then poll DescribeHumanLoop until HumanLoopStatus is Completed and read HumanLoopOutput.OutputS3Uri for the reviewer's verdict. Install with pip install jentic.
Where does the Amazon Augmented AI Runtime API store the reviewer's output?
Reviewer output is written to the S3 bucket configured in the flow definition, not returned inline. DescribeHumanLoop returns an OutputS3Uri pointing at the JSON object the reviewer produced; your application reads it directly from S3 once the loop status is Completed.
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