Reimagine AI agents with Amazon Bedrock AgentCore
Say you’ve perfected your AI agent’s logic.
It runs flawlessly on your machine and it's ready to change the world. But now comes the hard part: deployment. The thought of wrestling with servers, security, and scaling is daunting.
This is the year of AI agents. And while building them is half the battle, making them work reliably in production is where things get messy. From stitching tools and data sources together to handling infrastructure, scaling, and orchestration, deploying agents can feel like an endless puzzle.
But what if you could skip the chaos? What if you could launch your agent into a secure, production-ready environment as easily as you run locally?
AWS just introduced Bedrock AgentCore, a toolkit for implementing agents easily. It is designed to transform your AI agent ideas from a simple prototype into a production-ready application that millions can use.
In this newsletter, we’ll talk about the essential components of AgentCore, explaining what each one does and how they work together to create amazing AI experiences.
AgentCore Runtime: Core execution environment for SageMaker agents
Think of the AgentCore Runtime as the foundation where your AI agent lives and operates. It provides a framework-agnostic environment for running agents. In simple terms, you can deploy any local agent in LangGraph, LangChain, CrewAI, Strands, or any other custom agent framework, and deploy it to the cloud using AgentCore Runtime.
Here’s a list of key features of AgentCore Runtime:
