Securing AI agents with temporal policies in Amazon Bedrock AgentCore

Amazon Bedrock AgentCore has introduced temporal policies that allow for the enforcement of stateful, trajectory-aware rules governing AI agents. Unlike traditional stateless access controls, these policies evaluate requests in the context of an agent's session history, ensuring actions follow predefined sequences and constraints.

Temporal policies operate at the gateway perimeter, outside the agent's own code, making them tamper-proof and effective regardless of the agent's internal logic or potential bugs. They are particularly useful for enforcing workflow sequences, preventing data fabrication, capping financial exposure, and requiring human approval for high-value actions.

The policies work by tracking agent trajectories—bounded sequences of actions identified by session IDs—and evaluating each request against historical events within that session. This approach enables complex control patterns, such as ensuring certain steps occur in order, verifying data freshness, and limiting cumulative risk.

An example scenario involves a private banking agent managing client portfolios. Temporal policies can enforce that the agent retrieves client profiles before loading portfolios, ensures market data is recent before executing trades, caps total trading volume per session, and requires human approval for large trades. These controls are implemented through declarative policies that evaluate requests based on session history, providing a robust security layer.

Overall, temporal policies in Amazon Bedrock AgentCore offer a powerful mechanism for operational governance of AI agents, ensuring compliance, security, and risk management in sensitive applications.