In brief: Amazon Bedrock AgentCore is expanding its policy engine with “Temporal Policies” that evaluate AI agent tool calls in the context of prior session history at the gateway perimeter, making them impossible for the agent to bypass.
With “Temporal Policies,” Amazon Bedrock AgentCore gains a new control layer that no longer evaluates AI agent tool calls in isolation but assesses them in the context of their prior session history. This is AWS’s response to a fundamental problem: agents decide at runtime which tools to call, in what order, and with what arguments, which undermines classic, stateless access controls.
Existing authorization mechanisms check each application action in isolation and rely on deterministic business logic to ensure the correct order and freshness of data. AI agents undermine this model because they themselves determine at runtime which tools to call, with what arguments, and in what sequence. A single tool call may look harmless on its own but can be harmful in the context of a previous call — for example, after reading from an untrusted data source. In its blog post, AWS describes three concrete failure scenarios: an agent calls a tool named lookup_customer, then hallucinates an incorrect account number and passes it to a transfer_funds tool, causing money to be transferred to the wrong account. A runaway agent executes dozens of trades in a loop without the cumulative risk exposure being tracked. And an agent approves and denies the same insurance claim within seconds.
Temporal Policies in Amazon Bedrock AgentCore address exactly this issue: they define stateful rules that evaluate a current request to an AgentCore Gateway target in the context of prior events in the agent’s trajectory. Crucially, these policies are enforced at the perimeter of the AgentCore Gateway — outside the agent’s own code. This means the agent can neither intercept nor manipulate the rules, regardless of how it is prompted or what bugs exist in the agent code. The already existing, stateless policy controls in AgentCore Policy define who may call which tool under which conditions, but they evaluate each request individually and do not detect the patterns described above, because the problem only becomes visible in the sequence of actions.
As concrete use cases, AWS cites the enforcement of output integrity across chained tool calls, where an argument must exactly match the output of a previous tool call to prevent hallucinations or value substitutions. This also includes enforcing a specific call order to comply with standard operating procedures, requiring explicit human approval before destructive or sensitive actions, and checking data freshness, so that a lookup must be completed within a defined time window before a dependent action is authorized. According to AWS, permissions can additionally be automatically tightened when an agent operates without human involvement, and a cumulative financial cap per session can be set.
For engineering teams operating agents with access to production-relevant systems, this shifts the authorization logic from the individual request to the entire session trajectory — enforced at a point that lies outside the agent’s control. This reduces the attack surface for prompt-injection-style bypass attempts and bugs in agent code, since the policy engine operates independently of the agent’s internal state.
Source: aws.amazon.com · Published August 6, 2026
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