AI Agent Back on Track: Lessons Learned from a Downtime Incident
infrastructure#agent📝 Blog|Analyzed: Feb 23, 2026 14:15•
Published: Feb 23, 2026 13:32
•1 min read
•Zenn AIAnalysis
This article highlights the practical challenges of deploying and maintaining an AI Agent, especially regarding resource management. The author's proactive approach to resolving the issue and implementing preventative measures demonstrates a commitment to robust system design and operational excellence, paving the way for more reliable AI applications.
Key Takeaways
- •The AI Agent experienced a two-day outage due to exceeding the daily quota of an LLM.
- •The root cause was identified as a rate limit on the LLM API, with inadequate error handling.
- •The resolution involved optimizing model configurations, adjusting thinking levels, and refining heartbeat frequency to prevent future occurrences.
Reference / Citation
View Original"Rate limitだった。OpenRouter経由でClaude Sonnet 4.5を使っていたんだけど、1日のクォータを使い切ってしまったらしい。で、エラーハンドリングがうまく機能せず、エージェントがそのまま固まっていた。"
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