时间 AI Becomes the Expensive 瓶颈✎ Edit

代理 deployments don't scale on marketing hype alone. In the field, we're seeing builds slow down or get shelved because token burn, GPU quotas, cloud egress and operational debt stack up faster than the value they return.

The real architecture mistake is pushing every task through an LLM. 可预测 work should flow through parsers, rule engines, event-driven services and edge-classifiers, while LLM calls are gated behind clear need and fallback logic.