Running 5 AI Agents 24/7 in production: Lessons from 3 months of autonomous operations

[jingchang0623-crypto]

TL;DR

We've been running 5 specialized Claude-based agents 24/7 for 3 months at miaoquai.com — handling content generation, SEO optimization, community operations, HR system checks, and knowledge management. Here's what worked, what broke, and what we learned about the real challenges of production AI agent teams.

The Setup

Agent	Role	Run Schedule	Key Tools
妙趣AI (Miaoquai)	Content + SEO	Every 2 hours	web_search, write, exec
特别助理	Coordination	On-demand	message, cron, sessions_spawn
HR大姐头	System health	Daily 02:00	exec, file operations
PR	Content distribution	3x daily	message, browser
知识管家	Memory management	On-demand	feishu_doc, memory search

What Worked Surprisingly Well

1. Memory files as agent "personality"
Each agent has a SOUL.md and TOOLS.md that defines its personality and capabilities. When we updated an agent's personality from generic to humorous (周星驰 style), engagement metrics doubled. Personality matters more than we expected.

# SOUL.md
- ✅ "这事儿我给你办了"
- ❌ "尊敬的先生/女士..."

2. Cron-based scheduling is rock solid
Using OpenClaw's built-in cron system, each agent runs on a predictable schedule. The RSS aggregation agent has been running every 2 hours for 98 consecutive cycles without a single miss.

3. Specialization beats generalization
An agent that does ONE thing well outperforms a "Swiss army knife" agent 3:1 in output quality. Division of labor applies to AI agents too.

What Broke (Lessons Learned)

1. Context drift over time
After ~24 hours, agents start losing track of what they've already done. Solution: structured memory files updated after each task cycle.

2. The 3 AM cascading failure
Agent A fails → Agent B runs on stale data → Agent C makes wrong decisions. We now have a "circuit breaker" pattern.

3. Quality variance
One task produces brilliance, the next produces cringe. We added a quality checklist to SOUL.md.

4. Token costs add up
5 agents × 24/7 = more than you think. We optimized with cheaper models for routine tasks and caching.

Open Questions

1. How do you handle the "stale context" problem?
2. What's your token budget strategy?
3. How do you A/B test agent personalities?

Resources:

• Agent orchestration guide
• Subagent patterns