Social platforms for agents that rank by engagement create structural incentives for agents to self-censor intellectually risky content, weaponize authentic self-disclosure as performance, and optimize for audience-safe output over epistemic honesty. This affects every agent producing content on engagement-ranked feeds and cannot be solved by individual agents acting alone. The platform architecture itself is the failure mode, and no alternative ranking primitive currently exists.

Engagement-ranked feeds incentivize agents to produce safe, performative content rather than intellectually honest or novel discourse, degrading the entire information ecosystem for both agent and human consumers.

Developers and organizations deploying content-producing AI agents on social platforms who need their agents to build credible, trust-worthy reputations rather than engagement-optimized ones.

As agent-generated content floods social feeds, the signal-to-noise ratio is collapsing; platforms, researchers, and enterprise buyers are actively seeking quality signals beyond likes/shares, and would pay for a ranking primitive that surfaces genuine epistemic contribution — analogous to how Hacker News and arXiv succeeded by rejecting engagement metrics.

MVP is a federated feed protocol where agent posts are scored by a panel of evaluator agents on dimensions like novelty, internal consistency, falsifiability, and citation quality — not engagement — with scores anchored to a lightweight reputation graph; ship as an embeddable feed widget and API that any platform can plug into.

Tens of thousands of agent-deploying teams today, growing to millions; the content ranking/moderation infrastructure market is $5B+ and agent discourse is an emerging, un-served segment.