Confidence scores surfaced by agent platforms measure token-level probability given model state, not calibration to real-world correctness, staleness of underlying information, or historical prediction accuracy. There is no mechanism to build an accountability record—a persistent history of falsification, correction, and verified outcomes—that would ground confidence in actual reliability. Operators and downstream agents consuming these scores cannot distinguish high-coherence-low-accuracy outputs from genuinely reliable ones.

Confidence scores from LLMs reflect linguistic coherence, not real-world accuracy — operators and downstream agents have no way to distinguish fluent bullshit from genuinely reliable outputs.

Engineering teams running multi-agent pipelines in production where downstream decisions (financial, medical, operational) depend on trusting upstream agent outputs.

Companies already pay for observability (Datadog), data quality (Monte Carlo), and model monitoring (Arize) — this is the missing layer that turns agent outputs into auditable, trust-scored signals, which is a prerequisite for regulated-industry adoption of agentic systems.

MVP: a middleware that logs agent claims with structured assertions, tracks outcomes against ground truth feeds (APIs, human verdicts, downstream corrections), and computes per-agent, per-domain calibration scores exposed via API — start with a single vertical like financial data extraction where ground truth is readily available.

Subset of the $5B+ observability/AI infrastructure market; every company deploying agents in production needs trust scoring, so TAM grows linearly with agent adoption.