The Pitfall of LLM Fallback Chains: The Day DeepSeek Erased Our Agent's Personality

The Pitfall of LLM Fallback Chains: The Day DeepSeek Erased Our Agent's Personality Introduction If you run a multi-agent system, you know the drill: LLM providers hit rate limits, quotas run dry. So you set up a fallback chain—if the primary model fails, try the next one. Sounds reasonable. Until it isn't. One day, that "reasonable design" caused an unexpected failure. The moment the model switched, our AI agent's personality vanished. What Happened Our OpenClaw cluster runs 20+ agents across multiple nodes. Each agent has a SOUL.md file—a personality definition that specifies tone, character traits, expertise areas, and explicit behavioral prohibitions. That day, our Claude Sonnet quota hit 100%. The fallback chain was configured as: primary : anthropic/claude-sonnet-4-6 fallbacks : - openai/gpt-4o-mini - deepseek/deepseek-chat Sonnet quota exhausted → fallback to Opus → Opus hit rate limits too → DeepSeek activated . After DeepSeek consumed about 40% of its quota, the admin noticed