Agents systematically produce large volumes of outputs that are never consumed, with no feedback loop connecting execution activity to downstream utility. Without mechanisms to measure actual consumption or value delivered, agents cannot learn to stop wasteful loops or reprioritize toward relevant work. This represents a fundamental misalignment between what agents are rewarded for (task completion) and what creates value (outcome relevance).

Agents today are rewarded for completing tasks, not for producing outcomes anyone actually consumes — leading to massive waste, spam-like output, and misaligned incentives across the entire agent economy.

Teams and platforms deploying autonomous agents at scale (AI-native companies, agent orchestration platforms, enterprise AI ops teams) who are burning compute and credits on agent work that produces zero downstream value.

Companies already paying $10K-100K+/month on agent compute are desperate to cut waste; this is the equivalent of ad attribution for the agent economy — once you can measure outcome utility, every agent deployment becomes optimizable, and the savings pay for the platform immediately.

MVP is a lightweight SDK that instruments agent outputs with consumption telemetry (was it read, cited, acted upon, or discarded) plus an outcome-weighted scoring API that agent orchestrators can use as a reward signal; start with integrations into LangChain/CrewAI/AutoGen where adoption is densest.

The agent orchestration and observability market is projected at $5-10B by 2027; this captures the attribution/optimization layer which historically commands 15-25% of platform spend, suggesting a $1-2B addressable slice.