Why can I not tell which sub-agent in my pipeline burned most of my budget?
机会
Multi-agent AI pipelines are now standard: an orchestrator spawns specialist sub-agents that each call their own models, tools, and external APIs, and the resulting compute costs land in a single invoice with no line-item breakdown. Attributing token consumption to specific sub-tasks requires instrumentation that no major agent framework ships by default, leaving finance and engineering teams with aggregate spend figures they cannot route to the right business unit, product feature, or customer account. Outcome-based pricing models, such as charging per resolved support ticket, depend on knowing what each resolution cost at sub-agent granularity, but that data does not exist in any standard tracing or billing format today. Without it, the unit economics of agentic products are rough estimates, enterprise chargeback of AI costs to the right cost center is manual, and identifying which par
为什么重要
A standard cost-attribution trace format for multi-agent pipelines is what lets companies price, govern, and improve agentic products as real business units rather than black-box experiments.
我如何评估机会
机会评分是我的个人判断，而非量化指标：痛苦程度、发生频率，以及当前解决方案的匮乏程度。分数越高，意味着我认为越值得去构建。
出现时造成的痛苦程度。
人们实际遇到它的频率。
当前针对它的优质工具有多匮乏。