The article investigates a 27B model quantized with BF16, FP8, or INT4 that initially works then fails with malformed JSON and erroneous tool calls, indicating that quantization artifacts—not model capacity—are causing the breakdown. It explains that these quantization formats introduce precision errors that can corrupt output and trigger invalid tool invocations in local AI agents. The author argues that choosing the appropriate quantization format is critical for reliable agentic workloads.