AWS AI Agent Core Implementation

Amazon Bedrock AgentCore is an agentic platform for building, deploying, and operating highly effective agents securely at scale using any framework and foundation model.

With AgentCore, you can enable agents to take actions across tools and data with the right permissions and governance, run agents securely at scale, and monitor agent performance and quality in production - all without any infrastructure management.

AgentCore supports open frameowrk models such as

1. LangGraph
2. CrewAI
3. LlamaIndex and
4. Strands Agents

There are basically two major steps in deploying agents using AgentCore

1. Create Agent
2. Deploy Agent
3. AgentCore Invoke

Create Agent --> Choose a Framework and Model --> Integrations--> New Agent

Deploy Agent --> Basic Starter Deploy --> Prod Deploy --> Configure Runtime

AgentCore Invoke --> Invoke AgentCore using CLI - Agent Response.

Making Amazon Bedrock AgentCore agent Stateful by adding Memory

This is a simple agent to which you can add memory to make it stateful and completely change the narrative that AI Agents are always stateless. This Agent Core Memory is a stateless service which can be used as a short term or long term memory without much of an infrastructure or cost.

AgentCore Memory addresses a fundamental challenge in agentic AI: statelessness. Without memory capabilities, AI agents treat each interaction as a new instance with no knowledge of previous conversations. AgentCore Memory provides this critical capability, allowing your agent to build a coherent understanding of users over time.

Refer to the Hands on Guide on Implementing this in the next part of this article

References:
https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html
https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/agentcore-get-started-toolkit.html
https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-building.html