Securing AI agents with temporal policies in Amazon Bedrock AgentCore should be explained through that lens before any broad claim is made. Before AI agents, it was generally sufficient for access controls to treat each action as an independent event. Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that determine authorization to AgentCore Gateway targets by evaluating the current request in the context of prior events in an agent’s trajectory. The piece keeps the context, impact, and follow-up signals in view so readers do not stop at the headline.
What happened
Before AI agents, it was generally sufficient for access controls to treat each action as an independent event. Applications relied on deterministic business logic to enforce whether actions happened in the right order or whether the data was up-to-date. AI agents behave in fundamentally different ways than traditional applications. They decide at runtime which tools to call, with which arguments, and in what order. That flexibility, combined with increasingly intelligent models, makes agents equal measures capable and challenging to control. One tool call might be deemed safe when considered in isolation, but harmful in the context of the preceding call, such as after reading from an untrusted data source.
Practical impact for readers
Temporal policies in Amazon Bedrock AgentCore let you define stateful rules that determine authorization to AgentCore Gateway targets by evaluating the current request in the context of prior events in an agent’s trajectory. Because these policies run at the AgentCore Gateway perimeter, outside the agent’s own code, the agent cannot intercept or manipulate them.
Details worth verifying
In this post, you will learn what temporal policies are, how they work, and walk through an example to demonstrate. We will show you how to use temporal policies to enforce workflow sequencing, prevent data fabrication between tool calls, cap cumulative financial exposure per session, and require human approval for high-value actions. You will also see how to automatically tighten permissions when an agent operates without human engagement. First, however, we will explore the needs and use cases for stateful policies in more detail.
Who should act or wait
Existing access controls in AgentCore Policy enforce stateless, deterministic rules on each individual request: who can call which tool, under what conditions. Stateless controls are necessary but often insufficient for agents. Consider the following scenarios where existing stateless controls fail to catch critical issues:.
What is still unclear
Each individual tool call in these scenarios would pass a stateless policy check. The problem only becomes apparent when you look at the agent’s trajectory, the ordered sequence of actions in a session. Temporal policies extend Policy in AgentCore with this trajectory-aware enforcement layer. Temporal policies run at the gateway, outside the agent’s code, so they cannot be bypassed regardless of what the agent does, how it is prompted, or what bugs exist in the agent code. Some common temporal policy use cases include:.
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