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AWS EC2 lifecycle manager with AI chat agent (describe, start, stop, reboot)

Trung TranTrung Tran
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2/3/2026
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EC2 Lifecycle Manager with AI Chat Agent (Describe, Start, Stop, Reboot, Terminate)

Watch the demo video below:

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Who’s it for

This workflow is designed for DevOps engineers and cloud administrators who want to manage AWS EC2 instances directly from chat platforms (Slack, Teams, Telegram, etc.) using natural language.
It helps engineers quickly check EC2 instance status, start/stop servers, reboot instances, or terminate unused machines — without logging into the AWS console.

How it works / What it does

  1. A chat message (command) from the engineer triggers the workflow.
  2. The EC2 Manager AI Agent interprets the request using the AI chat model and memory.
  3. The agent decides which AWS EC2 action to perform:
    • DescribeInstances → List or check status of EC2 instances.
    • StartInstances → Boot up stopped instances.
    • StopInstances → Gracefully shut down running instances.
    • RebootInstances → Restart instances without stopping them.
    • TerminateInstances → Permanently delete instances.
  4. The selected tool (API call) is executed via an HTTP Request to the AWS EC2 endpoint.
  5. The agent replies back in chat with the result (confirmation, instance status, errors, etc.).

How to set up

  1. Add Chat Trigger

    • Connect your chatbot platform (Slack/Telegram/Teams) to n8n.
    • Configure the “When chat message received” node.

  2. Configure OpenAI Chat Model

    • Select a supported LLM (GPT-4, GPT-4.1, GPT-5, etc.).
    • Add system and user prompts to define behavior (EC2 assistant role).

  3. Add Memory

    • Use Simple Memory to keep track of context (e.g., instance IDs, region, last action).

  4. Connect EC2 API Tools

    • Create HTTP Request nodes for:
      • Describe Instances
      • Start Instance
      • Stop Instance
      • Reboot Instance
      • Terminate Instance
    • Use AWS credentials with Signature V4 authentication.
    • API endpoint: https://ec2.{region}.amazonaws.com/

  5. Link Tools to Agent

    • Attach all EC2 tools to the EC2 Manager AI Agent node.
    • Ensure the agent can choose which tool to call based on user input.

Requirements

  • n8n instance (self-hosted or cloud).
  • Chat platform integration (Slack, Teams, or Telegram).
  • OpenAI (or other LLM) credentials.
  • AWS IAM user with EC2 permissions:
    • ec2:DescribeInstances
    • ec2:StartInstances
    • ec2:StopInstances
    • ec2:RebootInstances
    • ec2:TerminateInstances
  • AWS region configured for API calls.

How to customize the workflow

  • Add safety checks: Require explicit confirmation before running Stop or Terminate.
  • Region flexibility: Add support for multi-region management by letting the user specify the region in chat.
  • Tag-based filters: Extend DescribeInstances to return only instances matching specific tags (e.g., env=dev).
  • Cost-saving automation: Add scheduled rules to automatically stop instances outside working hours.
  • Enhanced chatbot UX: Format responses into tables or rich messages in Slack/Teams.
  • Audit logging: Store each action (who/what/when) into a database or Google Sheets for compliance.

n8n AWS EC2 Lifecycle Manager with AI Chat Agent

This n8n workflow provides a conversational interface to manage AWS EC2 instances using an AI Chat Agent. It allows users to interact with an AI to perform actions like describing, starting, stopping, or rebooting EC2 instances through natural language commands.

What it does

This workflow simplifies AWS EC2 management by:

  1. Listening for Chat Messages: It acts as a chatbot, waiting for incoming messages from a configured chat service (e.g., Slack, Telegram, Discord, etc., depending on the Chat Trigger setup).
  2. Processing with an AI Agent: It feeds the received chat message into an AI Agent powered by OpenAI.
  3. Maintaining Conversation Context: A simple memory buffer is used to maintain the context of the conversation, allowing for more natural and continuous interactions with the AI.
  4. Generating Responses: The AI Agent, using the OpenAI Chat Model, processes the user's request and generates an appropriate response, potentially including actions to be taken on AWS EC2 instances (though the AWS action nodes are not explicitly defined in the provided JSON, this is the intended purpose of the AI Agent in such a workflow).

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: An API key for OpenAI to use the Chat Model. This will need to be configured as an n8n credential.
  • Chat Service Integration: A configured chat service (e.g., Slack, Telegram, Discord) connected to the n8n Chat Trigger node. This typically involves setting up a webhook or bot token within n8n's credentials.
  • AWS Credentials (Implied): Although not explicitly shown in the provided JSON, to perform actual EC2 actions (describe, start, stop, reboot), you would need AWS credentials configured in n8n and additional AWS EC2 nodes integrated into the AI Agent's toolset.

Setup/Usage

  1. Import the Workflow: Import the provided JSON into your n8n instance.
  2. Configure Credentials:
    • OpenAI Chat Model: Configure your OpenAI API Key as an n8n credential and select it in the "OpenAI Chat Model" node.
    • Chat Trigger: Configure your desired chat service (e.g., Slack, Telegram) with the "When chat message received" node. This usually involves setting up a webhook or bot token credential within n8n.
  3. Activate the Workflow: Once all credentials are set up, activate the workflow.
  4. Interact via Chat: Send messages to your configured chat service. The AI Agent will process your requests and respond.
  5. Extend with AWS EC2 Tools (Further Development): To enable actual EC2 management, you would need to:
    • Add AWS EC2 nodes (e.g., "EC2 Describe Instances", "EC2 Start Instances", "EC2 Stop Instances", "EC2 Reboot Instances") to your workflow.
    • Configure these AWS EC2 nodes with appropriate AWS credentials.
    • Integrate these AWS EC2 nodes as "tools" within the "AI Agent" node, allowing the AI to call them based on user commands.

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