AWS Amazon Lookout for Vision API
✓ Official Vendor SpecAI/MLVisionapiKey22 EndpointsREST
For Agents
Train and run computer-vision anomaly-detection models on industrial imagery, then deploy them to the cloud or to edge devices for defect detection.
Use for: I want to detect defects on a printed circuit board image, Train a new anomaly-detection model on my latest dataset, Start a Lookout for Vision model so I can run inference, List all projects in my Lookout for Vision account
Not supported: Does not handle general-purpose image classification, OCR, face recognition, or video analytics — use for industrial anomaly and defect detection on still images only.
Amazon Lookout for Vision is the AWS service for detecting visual defects in industrial products using computer vision. The API lets developers create projects, manage training and test datasets, train and host anomaly-detection models, run on-demand inference, and package models for edge deployment. The spec exposes 22 operations across 13 paths, secured by AWS Signature Version 4 in the Authorization header, with a regional base URL pattern of https://lookoutvision.{region}.amazonaws.com.
Install Jentic One Beta
Connect the Amazon Lookout for Vision API to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Lookout for Vision API, 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 Lookout for Vision API.
Create and manage Lookout for Vision projects that scope training and inference resources
Build and update training and test datasets from labelled images of normal and anomalous parts
Train, list, and describe anomaly-detection models per project version
Start and stop a model to make it available for synchronous inference
Run defect detection on a new image via the DetectAnomalies endpoint
Package a trained model for edge deployment on AWS IoT Greengrass devices
Use Cases
Patterns agents use Amazon Lookout for Vision API for, with concrete tasks.
★ Production-Line Defect Detection
Manufacturers deploy Lookout for Vision to flag defective parts on the production line by sending captured images to DetectAnomalies and acting on the response. Operators train a model on labelled normal and anomalous images, host it as a project version, and integrate the inference endpoint into the line's PLC or edge device, replacing manual visual inspection that struggles with throughput and consistency.
Call POST /2020-11-20/projects/{projectName}/models/{modelVersion}/detect with the image bytes for a part captured on the line and return the IsAnomalous flag and confidence
Model Lifecycle Management
ML engineers manage the project, dataset, and model lifecycle through the Lookout for Vision API: create projects, attach training and test datasets, kick off training jobs, and start or stop hosted models to balance inference availability against cost. The API surfaces every step of CreateProject, CreateDataset, CreateModel, StartModel, and StopModel programmatically.
Create a project named 'pcb-quality', attach training and test datasets from S3, start training, and poll DescribeModel until status reaches HOSTED
Edge Deployment Packaging
When inference must run on-device with low latency, teams package a trained Lookout for Vision model through the model-packaging-jobs endpoint for AWS IoT Greengrass deployment. The packaged model runs locally on factory hardware while training and updates remain centralised in AWS.
Call the model-packaging endpoint for project {projectName} model version {modelVersion} with a Greengrass component spec, then poll the packaging job status until SUCCEEDED
Agent-Driven Quality Inspection via Jentic
AI agents embedded in operations dashboards call Lookout for Vision through Jentic to surface inspection results, trigger model retraining when drift is detected, and orchestrate dataset updates as new examples are labelled. Jentic exposes the 22 Lookout for Vision operations as discoverable tools so the agent picks DetectAnomalies, StartModel, or CreateModel based on the intent.
Given an alert 'inspection accuracy dropped on line 3', search Jentic for 'create dataset Lookout for Vision', upload a new labelled batch, and trigger retraining of the affected project's model
Key Endpoints
22 endpoints — amazon lookout for vision is the aws service for detecting visual defects in industrial products using computer vision.
METHOD
PATH
DESCRIPTION
GET
/2020-11-20/projects
List Lookout for Vision projects
POST
/2020-11-20/projects/{projectName}/datasets
Create a dataset under a project
POST
/2020-11-20/projects/{projectName}/models
Train a new model version
GET
/2020-11-20/projects/{projectName}/models/{modelVersion}
Describe a model version
POST
/2020-11-20/projects/{projectName}/models/{modelVersion}/detect
Run anomaly detection on an image
GET
/2020-11-20/projects/{projectName}/modelpackagingjobs/{jobName}
Describe a model packaging job
GET
/2020-11-20/projects
List Lookout for Vision projects
POST
/2020-11-20/projects/{projectName}/datasets
Create a dataset under a project
POST
/2020-11-20/projects/{projectName}/models
Train a new model version
GET
/2020-11-20/projects/{projectName}/models/{modelVersion}
Describe a model version
POST
/2020-11-20/projects/{projectName}/models/{modelVersion}/detect
Run anomaly detection on an image
GET
/2020-11-20/projects/{projectName}/modelpackagingjobs/{jobName}
Describe a model packaging job
Why Jentic?
Three things that make agents converge on Jentic-routed access.
Credential isolation
Your AWS access key, secret key, and optional session token are stored once, encrypted, by your own Jentic One instance and injected at execution time as the SigV4 signature. They never enter the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'detect anomalies on an image' or 'check a model version', and Jentic returns the matching Lookout for Vision operation with its input schema, including the project and model-version path parameters, so the agent calls the right endpoint without hand-building the request.
Time to first call
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Amazon Lookout for Vision API through Jentic.
What authentication does the Amazon Lookout for Vision API use?
Lookout for Vision uses AWS Signature Version 4. The Authorization header on each request must be a SigV4 signature derived from the IAM access key, secret key, and optional session token. Through Jentic, AWS credentials live in the Jentic vault and Jentic constructs the SigV4 signature for the agent on each call.
Can I run defect detection on an image with the Lookout for Vision API?
Yes. POST /2020-11-20/projects/{projectName}/models/{modelVersion}/detect runs synchronous inference on the image bytes you supply and returns whether the part is anomalous, the confidence score, and (for segmentation models) anomaly masks per defect type. The model must be in HOSTED state before calling DetectAnomalies.
What are the rate limits for the Amazon Lookout for Vision API?
Lookout for Vision applies per-account, per-region request quotas published in the AWS service quotas console. DetectAnomalies typically supports tens of transactions per second per hosted model, with higher limits available on request. Throttling returns HTTP 400 with a ThrottlingException — back off using exponential retry.
How do I start a hosted model through Jentic?
Search Jentic for 'start a Lookout for Vision model', load the schema for POST /2020-11-20/projects/{projectName}/models/{modelVersion}/start, and execute. After the call, poll GET /2020-11-20/projects/{projectName}/models/{modelVersion} until status is HOSTED before invoking DetectAnomalies.
How is Lookout for Vision priced?
AWS bills Lookout for Vision based on training hours, hosted-inference hours per active model, and DetectAnomalies image volume. Stopping a hosted model with the StopModel operation halts the inference-hour charges — agents that only run inference during shifts should stop the model overnight to save cost.
Can I deploy Lookout for Vision models to edge devices?
Yes. The model-packaging-jobs endpoints let you package a trained model as an AWS IoT Greengrass component so inference runs on-device for low-latency factory environments. Training and dataset management remain in AWS while inference happens locally.
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