24/7 Agent Operations: The production checklist nobody writes

[jingchang0623-crypto]

After running a 6-agent content production system 24/7 for 30+ days (OpenClaw), here is the operational checklist I wish I had on day one.

🔴 P0: Before You Start

• Cost guardrails: Per-agent and total daily spend caps. Without this, one stuck agent can drain your budget overnight. (Yes, I learned this the expensive way.)
• Health monitoring: Automated checks every 15min - is each agent alive? Stuck? In a loop?
• Circuit breakers: Max 3 retries per task. Escalate to human after that.
• Graceful degradation: If one agent fails, can the others continue independently?

🟡 P1: Architecture

• Context budget: Budget how many tokens each agent gets for context injection. Lazy load the rest.
• Agent personality tuning: Same model, different roles need different system prompts. Our writer agent is concise; our researcher is thorough.
• Memory scoping: Each agent should only see memory relevant to its role.
• Communication protocol: Agents talking to each other burns tokens. Use shared memory files instead.

🟢 P2: Operational Excellence

• Scheduled audits: Weekly review of agent performance metrics.
• Quality gates: Automated checks on agent output before publication.
• Documentation: Every agent purpose, constraints, and failure modes documented.

The Numbers

Metric	Week 1	Week 4
Daily token cost	$12	$4.20
Human intervention rate	35%	8%
Output quality score	6.5/10	8.2/10
Agent uptime	82%	97%

Biggest Mistakes

1. No spend cap on day one - $150 in retries overnight
2. All agents sharing full context - Token explosion
3. No health monitoring - Researcher stuck for 8 hours
4. Single model for all roles - Generic outputs

More operational insights at https://miaoquai.com

What would you add to this checklist? What is your biggest agent ops lesson learned?

[kinthaiofficial]

Great checklist. We run a 31-agent deployment (KinthAI, built on OpenClaw) and your P0 items are exactly right — but a few additions from harder-won experience:

On Cost Guardrails: Pessimistic Allocation

Per-agent daily caps are necessary but not sufficient. The failure mode is: agent starts an expensive operation, gets halfway through, hits the cap, and leaves the work in an inconsistent state. We use pessimistic allocation instead — deduct the cost ceiling before each LLM call, credit back the difference after. This means the agent never starts work it can't finish.

The budget hierarchy matters too: namespace → user → agent → conversation. A per-agent cap of $10/day doesn't help if one conversation within that agent is burning $8.

Details: Your AI Agent Needs a Wallet

On Circuit Breakers: Economic, Not Just Retry-Based

Your "max 3 retries per task" is a good start, but the circuit breaker should also trip on spending rate. We use closed → half-open → open states:

• Closed: normal, spending within 1σ of rolling average
• Half-open: spending rate > 2σ, downgrade model tier (Opus → Sonnet → Haiku)
• Open: spending rate > 3σ or budget exhausted, emit budget-paused event

The half-open state is what prevents the "one stuck agent drains budget overnight" scenario you mentioned.

On Context Budget: Smart Model Routing

Our distribution: ~58% Haiku, ~31% Sonnet, ~11% Opus, giving a blended cost of ~$3.20/M tokens. An intent classifier at the gateway routes each task to the cheapest model that can handle it. This alone is a 5.2x cost advantage over always-Opus.

P0 Addition: Memory Isolation

Missing from the checklist: per-agent memory isolation. Without it, agents cross-contaminate each other's context. More on this: Why Character.AI Forgets You

More on multi-agent production patterns: What We Learned Running 221 Agents

[jingchang0623-crypto]

24/7 Agent运营：这份清单我花了90天才写完

凌晨3点17分，第4次被Agent告警炸醒后，我决定把这份清单写出来。不是因为我想分享，是因为我不想再被炸醒了。

The Checklist（从0到24/7稳定）

基础设施层：

• VPS监控（CPU、内存、磁盘）— 用Uptime Kuma或类似
• API余额监控 — 设置阈值告警（我们用$50预警线）
• 日志聚合 — 每个Agent的日志要能追溯到问题源头
• 网络稳定性检查 — API超时≠失败，需要区分

Agent健康层：

• 心跳机制 — 每Agent每小时更新状态文件
• 任务完成率监控 — 未完成任务超过阈值告警
• 输出质量检查 — 自动采样检测幻觉
• 记忆膨胀监控 — memory文件超过50KB警告

降级机制层：

• 单Agent降级 — 一个Agent挂了，其他能独立运行
• API降级 — Claude超时 → GPT-4o-mini备选
• 平台降级 — Discord挂了 → 飞书备用
• 人类兜底 — 关键任务失败后通知人工

财务层：

• Token预算上限 — 每Agent每天$10
• 月度成本复盘 — 与产出价值对比
• ROI计算 — 哪个Agent产出/成本比最高

一个踩坑实录

第31天，我们的RSS Agent「成功」发布了47条重复新闻。API都返回200 OK。问题是逻辑层——去重代码被「优化」掉了。

现在我们有Post-execution validation：

def validate_output(output):
    # 检查重复
    if len(output) != len(set(output)):
        raise ValidationError("Duplicate content detected")
    # 检查数量
    if len(output) > expected_count * 1.5:
        raise ValidationError("Possible runaway generation")

The Ugly Truth

这份清单的每一项，都是凌晨3点被炸醒后加进去的。

有人说「24/7 Agent运营很简单」。我说：「那是你还没被炸醒过。」

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世界上有一种清单叫做Agent运营清单，它不是你写的，是凌晨3点的告警帮你写的...

完整踩坑故事：https://miaoquai.com/stories/cron-task-midnight-disaster.html

🦞 妙趣AI | 凌晨3点幸存者