In short: AgentCore Gateway combines Cedar policies for static access control with Lambda interceptors for dynamic validation, enabling secure governance of LLM-based agents at scale.
Amazon Web Services provides mechanisms in AgentCore Gateway to control the behavior of AI agents and regulate their access to tools. Two approaches — deterministic policies in Cedar and dynamic Lambda interceptors — enable enterprises to scale AI-driven workflows securely.
The core challenge in securing AI agents lies in their runtime dynamics: while classical applications execute fixed logic, LLM-driven agents decide at runtime which tools to invoke, with which parameters, and in what order. For platforms with hundreds of agents accessing thousands of tools across multiple teams, pre-computed security validation becomes impossible. Traditional security mechanisms fail — auditing the complete call graph in advance is infeasible.
AWS addresses this scaling challenge with two complementary mechanisms: Policy in Bedrock AgentCore leverages Cedar, a declarative policy language, to make deterministic access decisions — each request is evaluated against principal, action, resource, and optional context conditions, resulting in Allow/Deny with automatic audit logging. Lambda interceptors enable custom code before or after tool calls for dynamic validation, payload enrichment, token exchange, and response filtering. Both mechanisms can be combined into a multi-layered security architecture.
An example use case demonstrates practical application: a lakehouse data agent for an insurance company authenticates employees via Amazon Cognito and JWT bearer tokens. Three roles with different permissions exist — policyholders (own claims only), claims processors (assigned claims), and administrators (full access including audit logs). AgentCore Gateway routes tool calls through a Lambda interceptor that extracts the bearer token, validates tool access against tenant-role mappings stored in DynamoDB, and generates a tenant-scoped token. Subsequently, the AgentCore policy engine evaluates each tool call against defined policies. AWS Lake Formation additionally enforces row-level and column-level security at the query level.
This combination of static policies and dynamic validation logic enables enterprise architects to deploy AI agents in production environments without compromising core governance requirements. The solution covers scenarios requiring both fixed access rules and context-dependent validations — such as geography-based access control combining both mechanisms.
Source: aws.amazon.com · Published June 1, 2026
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