A Juejin AI deep dive argues that AI agent spending is structural rather than simply price times usage, framing a fourth discipline — cost-control engineering — alongside harness, context and loop engineering. It opens with a reported Claude Code case in which a user's daily usage jumped from 161 million to 1.306 billion tokens ($41 to $493) because server errors triggered automatic retries that Anthropic customer service said count against billing without compensation. The piece then surveys caching hit rates, tool-definition bloat, Code Mode, context clearing, sub-agent isolation and budget circuit breakers, drawing on figures from Anthropic, Manus and ProjectDiscovery plus third-party harness benchmarks showing 7x to 70x cost differences. It concludes that a shorter correct path, not a cheaper model, is the real target.