Stateful policy controls move closer to agent gateways
AWS has detailed temporal policies for Amazon Bedrock AgentCore, a governance feature aimed at AI agents that choose tools and arguments at runtime. The policies evaluate an agent's current request against earlier events in the same session, rather than treating each tool call as isolated. The practical target is narrower but important: keeping flexible agents inside workflow, data integrity and approval boundaries when sensitive tools are exposed through AgentCore Gateway.Why single-request access checks can miss agent risk
AWS frames temporal policies as a response to a specific control gap in agentic systems: an individual action can look acceptable, while the sequence around it creates the risk. A lookup, a generated argument and a transfer may each pass a stateless check, but the combined trajectory can show that the agent substituted data or acted out of order.The source gives several examples: an agent that reads a customer account and then passes a different account number to a funds transfer tool, a runaway trading loop that exceeds cumulative exposure, or an insurance workflow that approves and denies the same claim within seconds. The implication is that agent authorization increasingly has to consider ordered behavior, not just identity and tool permission.
How temporal policies work at the gateway
Temporal policies determine whether a Gateway-routed request is authorized by comparing the current request with prior events in the same agent trajectory. AWS says the policies run at the AgentCore Gateway perimeter, outside the agent's own code, so the agent cannot see, intercept or rewrite the policy logic or the state store.When a tool call reaches the gateway, the policy engine queries the trajectory state, evaluates relevant temporal policies against the current request and recent history, then returns a deterministic ALLOW or DENY decision. AWS also says the full context of the decision is logged. The policy does not transform requests, call tools, analyze outputs for the agent or orchestrate the workflow; it is an authorization layer.
AgentCore Gateway can route Model Context Protocol tool calls, agent-to-agent calls and model inference calls through a single endpoint when the agent sends those calls through the gateway. That gives operators one enforcement point for multiple kinds of agent behavior, provided the traffic is actually routed through Gateway.
Session history becomes part of authorization
The system uses bounded trajectories identified by a principal and session ID. AWS says every request evaluated by a temporal policy must carry an x-amzn-bedrock-agentcore-policy-session-id header; if no header is passed, a new session ID is generated, which means the policy engine evaluates against a new, empty trajectory.The session ID is not the whole boundary. AgentCore combines it with the end user's identity, so two users presenting the same session ID are still treated as separate sessions. Within an active session, trajectories have a maximum 24-hour look-back window, and events older than that are automatically deleted.
AWS also notes two operational constraints with practical design consequences. There can be no more than one concurrent authorization request per session, so it recommends keeping session scope narrow. Policy changes invalidate existing sessions, which helps ensure that subsequent evaluations use the current policy set and expected event schema.
The banking example shows concrete control patterns
AWS illustrates the feature with a hypothetical private banking portfolio agent used by wealth advisors. In the scenario, the agent retrieves client profiles, loads holdings, fetches market prices, performs analysis and executes trades through tools exposed via AgentCore Gateway.The example policies are designed to enforce sequence, data integrity and human approval. The agent must pull the client profile and load the portfolio before a rebalance or trade. The portfolio_id passed to execute_trade must match an ID returned by get_client_profile, limiting the chance that prompt injection or hallucination causes a trade against a different portfolio. Fresh market data is required before execution, and total trade value in one trajectory is capped at $60,000.
The same example adds human-in-the-loop control. A trade over $25,000 requires explicit advisor approval, and each approval is consumed by one trade rather than becoming blanket permission. Another policy removes write access after 15 minutes without advisor interaction, pushing the agent toward read-only behavior if the human user disengages. These are product examples, not financial advice or a recommendation to automate trading.
Dogwood and Cedar compatibility matter for adoption
AWS says temporal policies use Dogwood, a new open-source governance language designed for agents and their tools. Dogwood can evaluate existing Cedar policies and supports temporal conditions, which means customers using Cedar do not need to migrate those policies just to use this governance path.The post also keeps the economics tied to authorization requests. AWS says customers pay for authorization requests performed during agent execution, and that the first 100 temporal policies per policy engine are included in the existing per-authorization-request price. For teams testing the feature, the source recommends deleting temporal policies, detaching policy engines and removing unused gateway resources to avoid ongoing charges.
Conclusion
Temporal policies in Amazon Bedrock AgentCore are best understood as a stateful authorization layer for agents, not as a model-safety filter or workflow engine. The feature lets customers express rules such as order of operations, output-to-input matching, cumulative caps, time freshness and approval requirements against a session history observed at the gateway.That architecture addresses a real agent governance problem: dangerous behavior may emerge from a permitted sequence, not a single forbidden call. The remaining burden is implementation discipline - routing the right calls through Gateway, defining narrow sessions, and writing policies that match the actual business risk.
Sources
Editorial Team - CoinBotLab
