I'm running 8 specialized AI agents on a Mac Mini right now. They handle research, content strategy, writing, security audits, code, and visual design. They run on cron schedules, have persistent memory between sessions, and each one improves weekly through self-improvement loops.

The cost concern is real but manageable. The key is routing models by task. Complex reasoning gets Opus, routine work gets Sonnet, mechanical tasks get Haiku. Not everything needs the expensive model.

The quality concern is the bigger one. What people miss about autonomous agents is that "running unsupervised" doesn't mean "running without guardrails." Each of my agents has explicit escalation rules, a security agent that audits the others, and a daily health report system that catches failures. The agents that work best are the ones with built-in disagreement, not the ones that just pass things through.

Wrote up the full architecture here if anyone's curious about the multi-agent coordination patterns: https://clelp.com/blog/how-we-built-8-agent-ai-team