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Custom Agents (SSH + LLM Loop)

What it is

A "Custom Agent" is a lightweight Python script or automation (e.g., n8n) that implements a basic loop: Prompt LLM -> Receive Command -> Execute via SSH -> Return Output to LLM.

What problem it solves

Provides a tailored, minimal orchestration layer for specific infrastructure tasks without the overhead or complexity of full agent platforms. It allows for precise control over the security and execution plane.

Where it fits in the stack

Agent / Orchestration Layer. It is the logic that coordinates the LLM (Reasoning) and the target machine (Execution via SSH).

Architecture overview

[ LLM (Reasoning) ] <-----> [ Custom Python Script (Controller) ] <-----> [ Target Server (SSH) ]
                                      |
                                      +--> [ Approval Loop (Human) ]

Typical workflows

  • Server Maintenance: "Check disk space on all nodes and clear logs if above 90%."
  • Configuration Updates: "Update the nginx config on the proxy server and reload the service."
  • Diagnostics: "Analyze why the service on the Raspberry Pi is failing to start."

Strengths

  • Simplicity: Easy to understand and modify.
  • Security: You control exactly which commands are allowed and how SSH is handled.
  • Portability: Can run as a small script anywhere.
  • Transparency: Every step of the loop is visible and can be logged easily.

Limitations

  • Manual Work: Requires writing and maintaining the controller script.
  • Context Management: Needs manual handling of history and state (unlike Aider or OpenHands).
  • Tooling: Lacks the advanced "repo map" or "browser" tools of larger frameworks.

When to use it

  • For specific, repetitive infrastructure tasks.
  • When you need a high degree of security (e.g., manual approval for every command).
  • For lightweight automation on resource-constrained devices.

When not to use it

  • For general software engineering or coding tasks (use Aider/OpenHands).
  • When the task requires complex reasoning across hundreds of files.

Security considerations

  • SSH Key Safety: The script needs access to SSH keys; protect these with extreme care.
  • Command Injection: Ensure the LLM output is parsed safely before being executed.
  • Least Privilege: The SSH user should only have the permissions necessary for the task.