Running 6 AI Agents in Production: Architecture, Costs, and What Broke

For the past eight months, I have been running six autonomous AI agents as part of my company Zinin Corp. Not demos. Not notebooks. Production systems that wake up on schedules, check task queues, call APIs, publish content, and deploy code to a VPS in the Netherlands. This post breaks down the architecture, the actual costs, and three things that failed in production. Why Six Agents The short answer: I run too many concurrent projects for any one human to context-switch between effectively. Zinin Corp spans a career platform ( sborka.work ), a crypto marketing marketplace (КРМКТЛ), an MCP-first job board (MCPHire), an AI content persona (Lisa Solovyeva across five platforms), and my own personal brand. Each of these has content requirements, infrastructure needs, and strategic coordination work. At some point, the overhead of "what do I work on next" started to cost more than the work itself. The agents do not replace thinking. They reduce context-switching overhead and handle the ope