Deploying AI agents in enterprise environments often means navigating complex infrastructure, security, and integration demands. While building AI prototypes is easier than ever, moving from a local demo to a secure, production-ready system can be daunting.
Amazon Bedrock AgentCore, now in preview, aims to simplify this journey, giving developers a robust toolkit to deploy and operate AI agents at any scale, on any model or framework, whether hosted on AWS or elsewhere.
AgentCore: Core Features for Serious Scale
- AgentCore Runtime: Delivers a secure, serverless, low-latency environment with session isolation. Developers can deploy and scale agents easily, integrating with open-source or custom frameworks while maintaining strict data security.
- AgentCore Memory: Handles both short-term and long-term memory management, allowing agents to retain context and personalize experiences using advanced semantic memory techniques.
- AgentCore Observability: Brings deep insight into agent performance through dashboards, trace visualization, custom scoring, and integration with monitoring tools like Amazon CloudWatch and Datadog.
- AgentCore Identity: Provides granular identity and access management, securing agent access to AWS and third-party services, including OAuth2 and secure API key storage in a dedicated token vault.
- AgentCore Gateway: Connects agents to APIs and AWS Lambda functions, ensuring seamless and secure integration with both internal and external systems.
- AgentCore Browser and Code Interpreter: Allows agents to automate web workflows using managed browser instances and execute generated code in isolated environments for enhanced automation.
From Local Prototype to Production: A Hands-On Example
Imagine building a customer support assistant as a prototype using simulated tools. AgentCore lets you migrate this agent to the cloud with minimal code changes. By adopting AgentCore's modular services, your agent instantly benefits from secure deployment, context-aware memory, access controls, observability, and integration with real business systems.
For instance, AgentCore Runtime ensures each user session is isolated, protecting sensitive data, while AgentCore Memory enables the agent to remember prior customer interactions. AgentCore Identity restricts access based on user roles, and AgentCore Gateway enables secure, authenticated API connections. Observability tools let teams track agent performance and resolve issues in real time, closing the gap between prototyping and production.
Developer-Centric Experience
AgentCore SDKs make it easy to layer services onto existing codebases. Developers can test agents locally, then deploy them to the cloud with familiar AWS tools and CLI workflows. Pre-built solutions on the AWS Marketplace further streamline development, letting teams focus on innovation, not infrastructure.
Security and Compliance by Design
Security is foundational in AgentCore. Session isolation, encrypted memory, detailed access controls, and a secure token vault help agents meet enterprise security and compliance requirements. Integration with existing identity providers and flexible deployment options fit diverse organizational needs.
Getting Started with AgentCore
AgentCore is available in preview in select AWS regions and is free to try until September 16, 2025, with standard AWS charges applying to supporting resources. Whether you're building customer support bots, automating workflows, or delivering new AI-powered services, AgentCore provides the building blocks for secure, scalable deployments from proof-of-concept to enterprise production.
Bottom line: AgentCore removes the infrastructure burden, so your team can focus on delivering real business value with AI. For more information, check out the AgentCore documentation and sample projects on GitHub.
Amazon Bedrock AgentCore For Enterprise AI Agent Deployment