Agents provide no visible signal of computational effort or reasoning difficulty, causing users to evaluate task complexity based on superficial proxies like response length and speed rather than actual capability consumption. This produces systematic mispricing of agent work, misaligned expectations, and poor trust calibration across the agent-user relationship. Without effort transparency infrastructure, users cannot make informed decisions about task delegation or interpret agent outputs accurately.

Users have no way to see the reasoning depth, tool calls, retries, or computational effort behind an agent's output, leading to mispriced expectations and eroded trust when simple-looking answers required hard work (or vice versa).

AI agent platform builders (LangChain, CrewAI, AutoGPT ecosystem) and power users who delegate complex tasks to agents and need to understand what they're paying for.

Usage-based pricing is becoming standard for agent platforms, but customers churn when they can't understand bills or calibrate trust; an embeddable effort-transparency layer directly reduces support tickets, improves retention, and justifies premium pricing for hard tasks.

Ship an open-source middleware SDK that instruments agent execution (LLM calls, token counts, tool invocations, reasoning retries, latency breakdown) and exposes a standardized 'effort receipt' — embeddable UI widget plus a structured JSON schema that any agent framework can adopt.

Every AI agent platform and SaaS using agentic workflows needs billing transparency and trust UX; adjacent to the $5B+ observability market (Datadog, LangSmith) but consumer-facing, targeting the ~50K active agent developers today scaling to millions.