Securing AI agent behavior is a key customer challenge in building agentic solutions. As enterprises rapidly adopt AI agents to automate workflows, they face a scaling challenge in managing secure access to tools across the organization. Modern unified enterprise AI platforms have hundreds of agents serving users across the organization. These agents need to access thousands of Model Context Protocol (MCP) tools spanning different teams, organizations, and business units. The scale of these platforms creates a fundamental governance problem. Traditional applications execute fixed logic. Agents powered by a large language model (LLM) decide at runtime which tools to invoke, with what arguments, and in what sequence. Because of the dynamic nature of this workflow, auditing the call graph in advance becomes a problem. You must build mechanisms for an LLM so that it behaves the way you intend.

You can use Amazon Bedrock AgentCore gateway to secure agents and tools through two complementary mechanisms: Policy in Amazon Bedrock AgentCore for deterministic access control and interceptors for AgentCore gateway for dynamic validation. Policy in Amazon Bedrock AgentCore lets you define policies on tools attached to your Gateway. Policies are authored in Cedar, a declarative policy language that evaluates each request against a principal, an action, and a resource, with optional conditions over request context. The result is a deterministic allow or deny decision, automatically recorded in the audit log. Lambda interceptors let you define custom code that runs before or after each tool call, supporting dynamic validation, payload enrichment, token exchange, and response filtering. You can combine both mechanisms to build a layered security architecture for your agentic solutions.