Introducing Amazon Bedrock AgentCore Identity: Secure Identity and Access Management for AI Agents

Unpacking the Challenges of Managing AI Agent Identities at Scale

Streamlining Enterprise AI Security with AgentCore Identity

Key Features of AgentCore Identity for Building Secure AI Agents

Implementing Secure Agent Authentication: A Practical Guide

Best Practices for Managing AI Agents and Their Access

Future-Proofing Your AI Infrastructure with Robust Identity Solutions

Conclusion: Building Secure, Scalable AI Agents with Amazon Bedrock AgentCore Identity

Get Started: Resources and Next Steps for Implementing AgentCore Identity

Unveiling Amazon Bedrock AgentCore Identity: A New Era in AI Agent Security

We’re thrilled to announce the launch of Amazon Bedrock AgentCore Identity, a cutting-edge identity and access management service designed specifically for AI agents. This innovative service empowers agent developers and administrators to safely access AWS resources and integrate with a variety of third-party tools, including GitHub, Salesforce, and Slack. With AgentCore Identity, organizations can manage identity and access at scale, ensuring that agents can act on behalf of users or themselves with pre-authorized consent—dramatically reducing the need for bespoke access control implementations.

The Challenge of Managing AI Agent Security

As businesses roll out AI agents in production environments, they confront a fundamental issue: how to efficiently manage identity and access at scale. The need for applications to authenticate users while ensuring that agents can access tools and services without compromising security is paramount. Adding to this complexity, AI agents often operate across diverse systems and on behalf of multiple users, requiring a meticulous audit trail and compliance with organizational policies—all while preventing data breaches.

In this post, we’ll delve into how AgentCore Identity addresses these complex security challenges, starting with identifying core security needs for enterprise AI agents, moving on to the architecture of AgentCore Identity, and wrapping up with best practices for enterprise-grade security.

Agentic AI Security at Scale

Creating secure AI agents for enterprise deployment introduces unique identity and access management concerns that traditional security models were not built to handle. Various authentication and authorization checks are needed at different points in an AI agent’s workflow.

Outbound Authentication: Understanding Access

AI agents interact with numerous resources to fulfill user-defined tasks. Outbound authentication presents specific challenges, such as:

• Acting on behalf of users: Agents need clearance to access user-specific resources (like Google Drive or Slack).
• Service-to-service authentication: Agents might require their own credentials.
• Token management: Ensuring OAuth tokens and API keys are securely stored to prevent exposure.

Compliance and Auditability

For regulated industries, every action by an agent must be traceable. This involves:

• Comprehensive audit trails: Logging who accessed what, when, and for whom.
• Data isolation: Preventing data from mixing across users.
• Regulatory compliance: Meeting data protection and privacy standards.

Introducing AgentCore Identity

AgentCore Identity provides a centralized solution for these challenges, facilitating seamless integration with AWS services and third-party tools through standard protocols like OAuth 2.0. Let’s look at its main features:

• Agent Identity Directory: A unified service for managing agent identities, serving as a single source of truth.
• Agent Authorizer: Validates whether a user or service can invoke an agent.
• Resource Credential Provider: Manages the configurations required for agents to access downstream servers.
• Resource Token Vault: Securely stores users’ OAuth access tokens and retrieves them for executing actions on their behalf.

Key Features

1. Distinct Agent Identities: Each agent gets a unique identity, integrating with existing identity systems.

2. Dual Authentication Model: Handles both inbound and outbound authentication seamlessly.

3. Secure Token Vault: Credentials are encrypted, tied to specific user-agent combinations, and managed with zero token sharing.

4. Seamless Integration: The AgentCore SDK simplifies integration through declarative annotations for automatic credential management.

Starting Your Journey with AgentCore Identity

To illustrate the functionality of AgentCore Identity, let’s walk through deploying a productivity agent tailored for GitHub management.

Step-by-step Guide

1. Set up Your Identity Provider: Use OAuth 2.0-compatible providers like Amazon Cognito or others suited to your enterprise setup.

2. Create Your Agent with JWT Authentication: Configure your agent to accept JWT tokens for secure user identification.

3. Configure OAuth Credential Provider for GitHub: Set up access credentials to communicate with GitHub services.

4. Implement the Agent with Automatic Token Handling: Use built-in SDK annotations to manage the OAuth flow effortlessly.

5. Deploy and Test: Launch your agent and observe how AgentCore Identity manages authentication and token storage securely.

Best Practices for Security

When implementing AgentCore Identity, adhere to these guidelines:

• Principle of Least Privilege: Agents should only have access to essential resources.
• Token Management: Use automatic refresh mechanisms and enforce expiration policies.
• Audit and Monitoring: Enable comprehensive logging and review of operations regularly.
• Multi-Tenant Security: For SaaS environments, separate credential management should be configured.

Conclusion

Amazon Bedrock AgentCore Identity redefines how organizations can secure their AI agents at scale, providing a robust, enterprise-grade solution that not only simplifies identity management but also enhances security. As the significance of AI agents in the corporate sphere continues to rise, so does the imperative for effective identity solutions. By securing your agents with this state-of-the-art service, you pave the way for intelligent, compliant, and trustworthy operations in a complex digital landscape.

Resources to Get Started

• Explore the Documentation: Amazon Bedrock AgentCore Documentation
• Try the Samples: GitHub Repository with Examples
• No Cost Preview: Experience AgentCore services for free until September 16, 2025.

Start building secure, scalable AI agents today, and transform your ideas into production-ready solutions.

About the Authors

• Rahul Sharma is a Principal Product Manager-Technical at AWS, specializing in CIAM and agent identity.
• Fei Yuan is a Principal Engineer at AWS with extensive experience in agent identity and customer IAM.
• Satveer Khurpa focuses on cloud-based architectures in generative AI solutions for clients across industries.
• Antonio Rodriguez helps businesses leverage innovations with Amazon Bedrock.

With the introduction of Amazon Bedrock AgentCore Identity, organizations can navigate the complexities of AI security, making AI agents not only intelligent but also secure and reliable.