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Lesson

24/7 offline agents on real hardware

A local 24/7 agent doesn't fail only because of tokens: it fails because of heat, RAM, VRAM, reboots, slow model loading, full disks, and tasks nobody stops. Design for operations before you sell yourself on autonomy.

  • Choose architecture based on Mac, GPU PC, mini PC, or VPS.
  • Separate inbox, worker, and model.
  • Create watchdog, logs, and resource limits.

Honest architectures

  • Mac M-series: comfortable, quiet, and a good dev environment; watch prefill and unified memory.
  • RTX 3090/4090: more VRAM and CUDA; watch heat, power draw, and drivers.
  • Mini PC: useful for orchestration, not for large models.
  • Cheap VPS: fine for n8n/queues; bad for heavy inference without a GPU.
Terminal
agent-runtime:
  inbox: sqlite_or_postgres
  worker_concurrency: 1
  max_runtime_minutes: 20
  max_steps: 12
  idle_sleep_seconds: 10
  healthcheck:
    - ollama_api_alive
    - disk_free_gt_10gb
    - memory_free_gt_2gb
    - no_repeated_error_3x
  on_failure:
    - pause_task
    - save_trace
    - notify_human

Local operations checklist

  • One worker to start.
  • Persistent queue so tasks aren't lost on reboot.
  • Per-task logs, not just global logs.
  • Final summary with commands, files, and errors.
  • GPU/CPU/time limit before retrying.

Official sources

  • Ollama API
  • LangGraph Persistence
  • n8n queue mode
  • Docker Compose
Project: 24/7 agent with inboxOOM and memory management
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How all courses connect

This is not a checklist. Start with the foundation, choose an outcome, and go deeper only when your project needs more control.

  1. 1Understand
  2. 2Apply or build
  3. 3Operate with confidence
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Choose an application

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Continue into the technical branch when you need to maintain code, data, or infrastructure.

02

Build with code

Prepare your environment, work with coding agents, and run models while keeping control of your projects.

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This branch prepares you to design and operate reliable AI systems.

03

Take systems to production

Combine retrieval, agents, evaluation, security, deployment, and model adaptation when the problem requires it.

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You do not need every course: choose the component your system needs and return as it grows.

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