If you build and automate workflows in n8n without writing much code, you’ve probably used its AI Agent node to add a model call to a workflow.
What happened
If you build and automate workflows in n8n without writing much code, you’ve probably used its AI Agent node to add a model call to a workflow. It’s a great start. But a production agent needs more than a single model call: memory that lasts beyond one run, tools it can actually use like a browser or a code sandbox, and room to work through longer tasks. Building that scaffolding yourself is the hard part.
Practical impact for readers
Amazon Bedrock AgentCore is a platform to build, connect, and optimize agents at scale, with any framework or model. AgentCore harness, a capability of Amazon Bedrock AgentCore, is now generally available and provides that scaffolding for you. A new open-source community node brings it into n8n’s visual editor. You get production agents with persistent memory, real tools, and the model provider of your choice, without writing infrastructure or agent code. You’re not tied to one model either. The node works with Amazon Bedrock, OpenAI, Google Gemini, and LiteLLM-supported providers, and you can switch providers between turns of the same conversation.
Details worth verifying
In this post, you will install the node, @aws/n8n-nodes-agentcore , and build agents step by step in the n8n editor. You will start with an agent that remembers a conversation, scope memory to individual users, add a code interpreter tool, give the agent skills, and finish by running an agent privately in your own virtual private cloud (VPC). The node is open source under the MIT license, and AgentCore harness is powered by Strands Agents , the open source agent framework from AWS.
Who should act or wait
An agent is more than a model. The model reasons, but the harness does the work around it: it runs the orchestration loop, calls tools, manages the context window, keeps state across turns, recovers from failures, and isolates each session. Building that layer is where most teams spend their time.
What is still unclear
AgentCore harness gives you that layer as a managed capability. You define an agent in configuration, including the model it uses, the tools it calls, the skills it has access to, and the instructions it follows, and AgentCore assembles and runs the loop for you. Each session runs in its own isolated environment with a filesystem and shell, memory across sessions, and web browsing. When configuration isn’t enough, you can export the harness to Strands code and keep running on the same system.
Latest comments
0No comments yet. You can start the conversation.