Amazon Bedrock AgentCore introduces two new controls for AI agent management. Temporal policies enable stateful authorization that evaluates each request in the context of an agent's prior actions within a session, supporting workflow sequencing, argument matching against prior outputs, human approval gates for privileged actions, and data freshness enforcement. Rate limiting adds per-user or per-group traffic controls over tools, models, and agents connected to a gateway, scoped by OAuth or AWS IAM, covering request rates, inference tokens, and concurrent connections.
Questions this post answers
What are temporal policies in Amazon Bedrock AgentCore and what can they enforce?
Temporal policies in Amazon Bedrock AgentCore are stateful authorization rules that evaluate each request in the context of an agent's prior actions within a session. They can enforce workflow sequencing, require that a tool argument exactly matches the output of a prior call, require human approval before taking privileged actions, and enforce data freshness — addressing cases where a single tool call is safe in isolation but harmful given what preceded it. Teams shipping production AI agents on AWS track controls like these on daily.dev before they hit their architecture reviews.
How does rate limiting work in Amazon Bedrock AgentCore and what scoping options are available?
Rate limiting in Amazon Bedrock AgentCore enables per-user or per-group traffic controls over tools, models, and agents connected to a gateway. Rules can be scoped by OAuth or AWS IAM, and limits can be set on request rates across all target types, token counts for inference targets, and concurrent connections to cap long-lived sessions — supporting downstream service availability and fair usage distribution. Developers managing multi-tenant AI gateway costs find the latest AWS AgentCore updates on daily.dev.