Securing AI agents with temporal policies in Amazon Bedrock AgentCore

This article explains temporal policies in Amazon Bedrock AgentCore, which enforce stateful authorization rules that evaluate AI agent requests in the context of their session history rather than as isolated events.

• Temporal policies run at the AgentCore Gateway perimeter, outside agent code, preventing circumvention through prompts or bugs
• Enforce workflow sequencing, output-to-input integrity, data freshness, cumulative budget caps, and human approvals across tool calls
• Use Dogwood, an open-source governance language compatible with existing Cedar policies, to define trajectory-aware rules
• Prevent hallucination attacks where agents fabricate values between tool calls or accumulate unchecked financial exposure
• Support progressive trust decay, requiring human re-engagement after periods of autonomous operation
• Demonstrated through seven policy patterns for a private banking portfolio agent managing client trades and portfolios

Temporal policies provide tamper-proof, declarative authorization for agentic systems by enforcing stateful rules at the gateway level, enabling secure autonomous operation without constraining agent flexibility.