Amazon Bedrock AgentCore Gateway: A Guide to Securely Accessing Private Resources

Introduction

AI agents are moving from experimentation to production, but in enterprise-grade deployments, enabling AI agents to securely access resources within private networks remains a critical challenge. Recently, AWS published a detailed technical guide on Amazon Bedrock AgentCore Gateway's ability to access private endpoints, offering enterprises a new pathway to building secure and reliable AI agent architectures.

Core Solution: Resource Gateway Opens Up Intra-VPC Channels

Amazon Bedrock AgentCore Gateway is a unified gateway service provided by AWS for AI agents. The key capability highlighted here is its deep integration with Resource Gateway. Resource Gateway is a managed construct that provisions Elastic Network Interfaces (ENIs) directly within a user's Amazon VPC — one ENI per subnet — enabling secure network connectivity from AgentCore Gateway to private endpoints.

The core advantage of this architectural design is that AI agents can access internal enterprise services without being exposed to the public internet. All traffic is routed within the VPC, significantly enhancing data security and compliance.

Two Implementation Modes: Flexible Adaptation to Different Needs

The solution offers two implementation paths: Managed mode and Self-Managed mode.

Managed Mode

In Managed mode, AWS handles the full lifecycle management of Resource Gateway, including ENI creation, configuration, and maintenance. Users simply specify VPC and subnet information to complete deployment quickly. This mode is ideal for teams looking to reduce operational complexity, significantly cutting down on infrastructure management workload.

Self-Managed Mode

Self-Managed mode gives users complete control over Resource Gateway, making it suitable for enterprises with strict network architecture customization requirements. Users can independently manage ENI security group rules, subnet selection, and routing policies, enabling more granular network control.

Three Real-World Scenarios Explained

The solution addresses three typical enterprise scenarios, covering the most common requirements for AI agent access to private resources.

Scenario 1: Connecting to Private Amazon API Gateway Endpoints

Many enterprises build internal API services through Amazon API Gateway, setting them up as private endpoints to restrict public access. Through the combination of AgentCore Gateway and Resource Gateway, AI agents can directly invoke these private APIs without additional public-facing entry points or VPN tunnels. This provides a secure and efficient path for API-based enterprise service integration.

Scenario 2: Integrating MCP Servers on Amazon EKS

The Model Context Protocol (MCP) is becoming an important standard for AI agent interaction with external tools. In this scenario, MCP servers are deployed within Amazon Elastic Kubernetes Service (Amazon EKS) clusters, and AgentCore Gateway accesses MCP services in the EKS cluster directly via Resource Gateway, enabling seamless connectivity between AI agents and containerized tool services.

Scenario 3: Accessing Other Private Resources

Beyond the two scenarios above, the solution also supports access to databases, internal microservices, and various other private resources deployed within VPCs, offering strong extensibility and versatility.

Technical Analysis: Why This Capability Matters

As AI agent adoption deepens within enterprises, the types of resources agents need to access are expanding rapidly — from large language model APIs to internal knowledge bases, from business databases to third-party tool services. However, enterprise security policies typically require these resources to be deployed in private networks without public exposure.

The combined AgentCore Gateway and Resource Gateway solution fundamentally resolves the tension between the openness AI agents require and the network isolation enterprises demand. By deploying ENIs within VPCs, all data transmission occurs over AWS's private network backbone, eliminating the security risks associated with public internet data transfer.

Meanwhile, the dual design of Managed and Self-Managed modes reflects AWS's product philosophy of balancing out-of-the-box usability with deep configurability — lowering the barrier for small and mid-sized teams while meeting large enterprises' needs for fine-grained network architecture control.

Outlook

As the MCP protocol gains wider adoption and AI agent frameworks mature, enterprise demand for secure access to private resources will continue to grow. AgentCore Gateway's newly released private endpoint access capability marks another significant move by AWS in the AI agent infrastructure space.

Looking ahead, we expect more cloud providers to introduce similar agent-level network security solutions, and enterprise AI agent deployment will evolve from a question of feasibility to one of security. For developers and architects building AI agent applications, gaining early familiarity with these security architecture patterns will lay a solid foundation for future production deployments.