Enterprise AI Agents Made Easier with Amazon AgentCore (Preview)

Overview

Amazon has introduced Amazon Bedrock AgentCore, a new suite of enterprise-grade services under the Amazon Bedrock ecosystem designed to simplify the development, deployment, and scaling of AI agents. These agents, which rely on foundation models (FMs), can now perform complex, autonomous tasks with enhanced security, observability, and modular tool integrations. Amazon Bedrock AgentCore reduces the operational overhead required to build production-ready agents while supporting any agent framework or model, whether hosted on Bedrock or externally.

Currently available in preview in select AWS regions, Amazon Bedrock AgentCore marks a significant leap forward in how businesses transition from proof-of-concept AI agents to reliable, scalable enterprise solutions.

Introduction

The evolution of AI has shifted from mere content generation to the rise of agentic AI systems, software agents capable of reasoning, planning, and executing tasks autonomously. However, moving from initial experiments to fully functional, production-grade AI agents often requires solving numerous infrastructure challenges, such as session management, identity access, memory systems, and observability.

With the introduction of Amazon Bedrock AgentCore, developers can now focus on crafting intelligent behaviors rather than wrestling with backend complexity. Whether you’re using open-source frameworks like LangGraph, Strands Agents, or CrewAI, Amazon Bedrock AgentCore’s modular services provide everything needed to operate and scale AI agents securely.

Building Production-Ready Agents with Amazon Bedrock AgentCore

Let’s walk through how Amazon Bedrock AgentCore streamlines the development of an AI-powered customer support assistant, from prototype to production.

1. Deploying AI Agents with AgentCore Runtime

Amazon Bedrock AgentCore Runtime allows developers to deploy AI agents in a secure, low-latency serverless environment. Each user session is isolated to prevent data leaks, which is crucial when handling sensitive customer data.

Agents can run in publicly accessible environments or within a VPC-only setup for private infrastructure access. Integration requires minimal code updates, using the AgentCore SDK to define the application’s entry point.

After local testing via the AgentCore CLI, developers can deploy their agents to the cloud and start interacting with them using simple JSON payloads.

2. Enhancing Agents with Memory Capabilities

Amazon Bedrock AgentCore Memory enables agents to have both short-term and long-term memory. Short-term memory ensures the agent can maintain coherent conversations within a session, while long-term memory allows it to recall user preferences or past facts across sessions.

You can configure strategies like semantic memory to extract structured knowledge from interactions and retrieve it via semantic queries.

This significantly improves user experience by enabling context-aware and personalized responses.

3. Identity and Access Management

Security is foundational for enterprise applications. Amazon Bedrock AgentCore Identity empowers agents to authenticate and interact with external systems, like Slack, Salesforce, or internal APIs, on behalf of the user.

Using secure OAuth2 token management, developers can define credential providers, implement scoped permissions, and use decorators to enforce function-level access requirements.

The secure token vault ensures minimal user friction while maintaining strict access controls.

4. Integrating Tools with AgentCore Gateway

Internal tools or third-party APIs can be exposed to the agent using AgentCore Gateway, standardizing access using the Model Context Protocol (MCP). Whether Lambda functions or OpenAPI specs, the gateway handles authentication, request transformations, throttling, and multitenancy.

It even supports tool discovery, allowing agents to automatically select the best tool for a given task, which is essential for building dynamic, context-aware workflows.

5. Unlocking Code Execution and Web Automation

Sometimes agents need to run custom code or interact with websites that don’t offer APIs. For these cases:

• Amazon Bedrock AgentCore Code Interpreter provides an isolated environment in which to run generated code safely.
• Amazon Bedrock AgentCore Browser gives agents access to managed browser sessions for web-based automation.

Together, these tools expand agent capabilities, especially in customer service and data extraction domains.

6. Observability and Monitoring

Visibility into agent behavior is crucial for debugging and optimization. Amazon Bedrock AgentCore Observability offers built-in dashboards that track:

• Latency and token usage
• Tool invocation timelines
• Error rates and performance metrics

AgentCore also supports OpenTelemetry, enabling seamless integration with observability platforms like Amazon CloudWatch, LangSmith, and Datadog. Developers can trace every agent decision, visualize execution steps, and inspect trajectories for continuous improvement.

Conclusion

Amazon Bedrock AgentCore represents a paradigm shift in how businesses build and scale AI-driven agents. From secure runtime environments and memory management to robust identity systems and detailed observability tools, Amazon Bedrock AgentCore enables teams to move from idea to production confidently and quickly.

With its modular architecture, support for any framework or model, and integrations across AWS and third-party systems, Amazon Bedrock AgentCore is poised to become the backbone for enterprise-grade agentic AI solutions.

Drop a query if you have any questions regarding Amazon Bedrock AgentCore and we will get back to you quickly.

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