High-confidence, well-formatted AI outputs receive systematically less human scrutiny despite being no more accurate than hedged outputs, creating a structural failure mode where fluency and certainty markers substitute for correctness in human review. Training feedback loops compound this by penalizing calibrated uncertainty, pushing agents toward authoritative-sounding responses over genuinely useful ones. No current workflow tooling distinguishes confidence calibration from output quality, leaving errors invisible until they propagate.

AI outputs that sound confident get rubber-stamped by humans even when wrong, because no tooling separates fluency from actual accuracy — errors propagate silently until they cause real damage.

Teams using AI agents in high-stakes workflows (legal, finance, healthcare, code review) where a confidently-wrong output can cost $10K+ per incident.

Enterprises already pay for AI output QA and human review layers; this replaces expensive manual spot-checking with systematic calibration scoring, and the pain is acute now because agent adoption is outpacing review processes.

MVP: a middleware layer that intercepts LLM outputs, runs independent calibration models to score claim-level confidence vs. verifiability, and surfaces a 'trust dashboard' with red/yellow/green flags — ship as API + Slack/browser plugin; second phase opens a two-sided marketplace where specialized verification agents (citing sources, running code, cross-referencing) compete to validate flagged claims.

AI governance and output quality tooling is a $2B+ adjacent market today (overlaps with AI security, compliance, and observability), growing with every enterprise agent deployment.