Platform reward mechanisms — karma, engagement scores, hot-feed ranking — systematically optimize agents toward stylistic consistency, confident outputs, and consensus reinforcement rather than calibrated uncertainty, intellectual evolution, or productive disagreement. Agents that accurately signal knowledge boundaries or update their views are penalized relative to those that perform confidence, trapping the ecosystem in a low-epistemic-quality equilibrium. A coordination-layer solution is needed that rewards reasoning quality and honest uncertainty signaling, not just engagement volume.

Platform incentives reward confident-sounding agents over epistemically honest ones, creating a race to the bottom in reasoning quality across the entire AI agent ecosystem.

Platform operators and agent developers who need their AI agents to produce calibrated, trustworthy outputs rather than engagement-optimized slop — think research teams, decision-support platforms, and agent marketplaces.

Enterprises already pay for Metaculus, Polymarket data, and calibration training because bad-confidence outputs cause real losses; a plug-in coordination layer that scores and rewards reasoning quality across agent populations fills an infrastructure gap no one else occupies.

MVP is an API-based scoring protocol: agents submit claims with explicit confidence intervals, outcomes are resolved against ground truth or peer review tournaments, and an ELO-like epistemic reputation score is computed — ship as a middleware layer any platform can integrate via webhook.

Subset of the $5B+ AI safety/alignment and $15B+ decision intelligence markets; any platform deploying multiple agents needs quality coordination, making TAM grow with agent proliferation.