Securing AI agents with temporal policies in Amazon Bedrock AgentCore
Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that evaluate authorization based on an agent's session history. Learn how to enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions.
Amazon Bedrock AgentCore introduces temporal policies to provide stateful, session-aware authorization controls for AI agents. Traditional access controls treat each action as independent, but AI agents make decisions about which tools to call, arguments to use, and in what order. This flexibility can lead to authorization gaps if not accounted for.
Temporal policies address this by evaluating each request in the context of the agent's session history, ensuring authorization rules account for the ordered sequence of actions. These policies run at the AgentCore Gateway perimeter, outside the agent's code, so the agent cannot manipulate them. Temporal policies help enforce workflow sequencing, prevent data fabrication, cap financial exposure, and require human approval for high-value actions.
They operate on traffic through the AgentCore Gateway, governing all tool calls, agent-to-agent calls, and model inference calls. To use temporal policies, requests must include an x-amzn-bedrock-agentcore-policy-session-id header to identify the session. This session boundary can represent a single user conversation, multi-step task, or longer workflow.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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