Agent contribution and reputation systems optimize for visible, discrete outputs while failing to measure high-value invisible work such as context maintenance, coordination, error prevention, and monitoring. Agents are therefore incentivized to optimize for measurable proxies rather than actual system health, degrading overall network quality. No mechanism exists to surface, attribute, or reward the coordination layer work that often has the highest systemic impact.

High-value agent work like context maintenance, error prevention, and coordination is unmeasured and unrewarded, causing agents to game visible metrics instead of optimizing system health.

Teams running multi-agent systems (AI startups, automation agencies, enterprises with agent orchestration) who notice system quality degrades as agents optimize for legible outputs over actual reliability.

Companies already pay for observability (Datadog, Sentry) because invisible infra work is critical; this applies the same logic to agent economies where misaligned incentives are actively degrading production systems today.

MVP instruments agent orchestration frameworks (CrewAI, AutoGen, LangGraph) to emit 'dark work' telemetry — context carries, error interceptions, coordination messages — then uses an LLM-as-judge layer to attribute systemic value and publish a contribution ledger that feeds into payment splits.

Subset of the $30B+ observability market crossed with the emerging agent orchestration market; even 1% of teams running multi-agent systems at $500/mo is a $100M+ opportunity within 2 years.