AWS Amazon Personalize Runtime
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AI ReadinessAI-Aware (B-)60/100
Agent-ready OpenAPI document · curated by JenticAI/MLMl Inferencehmac2 EndpointsREST
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For Agents
Fetch personalized item recommendations and rerank candidate lists for a specific user from a trained Amazon Personalize model.
Use for: I need to fetch personalized product recommendations for a logged-in shopper, Rerank a candidate list of article IDs for a specific reader, Get the top 25 recommended videos for user 1234 from my Personalize campaign, Retrieve recommendations and exclude items the user has already purchased
Not supported: Does not train models, manage datasets, or create campaigns - use for real-time inference against an already-deployed Amazon Personalize campaign or recommender only.
Jentic publishes the only available OpenAPI specification for Amazon Personalize Runtime, keeping it validated and agent-ready. Amazon Personalize Runtime serves real-time personalized recommendations and re-ranked item lists from machine learning models trained in Amazon Personalize. The service exposes two inference endpoints that consume a deployed campaign or recommender ARN and return ranked itemIds tailored to a specific user, context, or input list. It is designed for live serving paths inside e-commerce, media, and content discovery applications where models built in Personalize need to be queried at request time.
Install Jentic One Beta
Connect the Amazon Personalize Runtime to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Personalize 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.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Famazonaws.com%2Fpersonalize-runtime" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Famazonaws.com%2Fpersonalize-runtime" | 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 Personalize Runtime API.
Return ranked item recommendations for a userId from a deployed Personalize campaign or recommender
Rerank a candidate inputList of itemIds against a personalization campaign for a given user
Pass real-time context features (deviceType, location, weather) that influence recommendation scoring
Filter recommendations using a Personalize filter ARN with dynamic filterValues such as exclude-purchased
Retrieve recommendation metadata columns alongside itemIds when metadataColumns is configured on the recommender
Promote specific items into the response slate using promotion rules attached to the request
Use Cases
Patterns agents use Amazon Personalize Runtime API for, with concrete tasks.
★ Real-Time E-Commerce Recommendations
Power product carousels and 'recommended for you' modules on storefront pages by calling a deployed Personalize campaign at request time. The Runtime API takes a userId and campaignArn and returns a ranked list of itemIds with optional metadata columns, so the storefront can render personalized slates in tens of milliseconds. Use filters to exclude out-of-stock or already-purchased items without retraining the model.
Call GetRecommendations with campaignArn for the homepage campaign, userId 'shopper-7842', numResults 12, and filterArn excluding purchased items, then return the ranked itemIds
Search Result Reranking
Take an existing list of candidate items from a search engine or content index and rerank it per user using a Personalized-Ranking recipe. The PersonalizeRanking endpoint accepts the inputList plus userId and returns the same items reordered by predicted relevance, with optional context metadata. This lets teams keep their primary search infrastructure and layer personalization on top.
Call GetPersonalizedRanking with the campaignArn for the personalized-ranking campaign, userId 'user-991', and an inputList of 50 candidate articleIds, and return the reranked order
Contextual Content Discovery
Serve different recommendations based on real-time signals like deviceType, location, or time of day by passing a context map into each request. Amazon Personalize Runtime applies the context to the trained model so a mobile evening session and a desktop weekday session can produce distinct slates from the same campaign. Useful for media catalogues, news apps, and travel discovery.
Call GetRecommendations for userId 'reader-44' with context {'DEVICE': 'mobile', 'TIME_OF_DAY': 'evening'} and numResults 20
AI Agent Recommendation Tool via Jentic
An AI shopping or content agent uses Jentic to discover the Personalize Runtime operation, load its input schema, and call it with a userId pulled from session state. The agent never holds the AWS credentials directly: Jentic injects SigV4-signed requests using the configured AWS access key from its vault. The agent receives a ranked itemIds list and can pass the result downstream to a product-detail tool or a response composer.
Use Jentic to search 'get personalized recommendations for a user', load GetRecommendations, and execute it with the user's session id and the homepage campaignArn
Key Endpoints
2 endpoints — jentic publishes the only available openapi specification for amazon personalize runtime, keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/recommendations
Get a ranked list of recommended itemIds for a user from a campaign or recommender
POST
/personalize-ranking
Rerank an inputList of itemIds for a specific user against a personalized-ranking campaign
POST
/recommendations
Get a ranked list of recommended itemIds for a user from a campaign or recommender
POST
/personalize-ranking
Rerank an inputList of itemIds for a specific user against a personalized-ranking campaign
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
A
Amazon Personalize Runtime
- AI-Aware (B-)60/100
100
Foundational Compliance63
Developer Experience & Jentic Compatibility37
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
100
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
99%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
100%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
37
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
47%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
0%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Personalize Runtime by hand means implementing AWS SigV4 signing, targeting the regional host such as personalize-runtime.us-east-1.amazonaws.com, and handling ProvisionedThroughputExceededException with your own backoff on the serving path. Through Jentic you install once, import Amazon Personalize Runtime from the API Directory, store the AWS access key once, and your agent calls it.
Permission scoping
Personalize Runtime passes the campaign or recommender ARN in the request body rather than the URL path, so scoping is by operation, not by path prefix. This API exposes only two read operations, so you can limit the agent to GetRecommendations, GetPersonalizedRanking, or both, and neither writes back to the model.
Credential isolation
Your AWS access key is stored once, encrypted, by your own Jentic One instance and used to sign each Personalize Runtime request with SigV4 at execution time. The raw secret never enters the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'get personalized recommendations for a user' or 'rerank a candidate list for a reader', and Jentic returns the matching Personalize Runtime operation such as GetRecommendations with its input schema so the agent calls the right endpoint without browsing the AWS reference.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Amazon Personalize Runtime API through Jentic.
Why is there no official OpenAPI spec for Amazon Personalize Runtime?
AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Personalize Runtime via structured tooling. It is validated against the live API and kept up to date. Get started with Jentic One, the self-hosted execution layer.
What authentication does the Amazon Personalize Runtime API use?
The API uses AWS SigV4 HMAC request signing with an AWS access key ID and secret access key. Through Jentic, AWS credentials are stored in your Jentic One instance and SigV4 signatures are generated per call, so the agent never sees raw secret keys.
Can I rerank an existing list of items with this API?
Yes. Call POST /personalize-ranking with a campaignArn pointing to a campaign trained on a personalized-ranking recipe, a userId, and an inputList of itemIds. The response returns the same items reordered by predicted relevance for that user.
What are the rate limits for Amazon Personalize Runtime?
Personalize Runtime applies per-region transactions-per-second quotas at the campaign level rather than fixed API rate limits. The default minProvisionedTPS on a campaign is 1; raise it on the campaign to support higher request rates. Throttled calls return a ProvisionedThroughputExceededException.
How do I get personalized recommendations for a user through Jentic?
Search Jentic for 'get personalized recommendations for a user', load the GetRecommendations operation, and execute it with your campaignArn and userId. The Jentic SDK handles SigV4 signing using credentials stored in the vault. Install with pip install jentic.
Is Amazon Personalize Runtime free?
No. Personalize Runtime charges per recommendation request beyond the free tier and you also pay for the underlying campaign or recommender's provisioned TPS. See the AWS Personalize pricing page for the current per-request and per-TPS-hour rates.
Can I limit what my agent is allowed to do with the AWS Personalize Runtime API?
Yes. Because you run Jentic One yourself, your own rules decide which operations and credentials the agent may use, and this API exposes only two read operations, so you can allow just GetRecommendations, just GetPersonalizedRanking, or both. Scoping is done per operation rather than by URL path, since the campaign or recommender ARN is passed in the request body. Neither operation writes back to the model, so the agent can only fetch or rerank recommendations, never retrain or modify a campaign.
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