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Introduces VibeLifeBench, a benchmark of 200 long-horizon tasks across ten everyday-life domains for evaluating proactive and persistent LLM agents in a simulated multi-week living world. Current frontier models score low, highlighting the gap between existing agents and real-life assistance.
A developer describes running AI agents in a simulated restaurant world and finds that across different models and harnesses, the same three API-related mistakes dominate: guessing nonexistent endpoints, exceeding budgets, and acting on stale IDs. Shows persistent failure patterns despite fixing the system.