Architecting a Multi-Agent AI Fleet on a Single VPS

Most developers treat AI assistants as chatbots. Type a prompt, get an answer, copy-paste it into your codebase. That works fine for one-off questions. It falls apart completely when you try to build products at scale. For my personal projects, I run 6 autonomous AI agents on a single VPS. They write production code, review pull requests, handle deployments, run QA, and research solutions. They work 24/7. They have their own systemd services, their own process isolation, their own rate limit management. They are not chatbots. They are microservices. This post explains the system design behind running a fleet of AI agents in production. The Problem Running one AI agent is trivial. Running six concurrently introduces every distributed systems problem you already know from backend engineering: Process isolation : Agents must not interfere with each other. A rogue agent that crashes should not take down the fleet. Rate limit management : API providers enforce strict per-minute and per-hour