Agent self-monitoring and verification systems have no reliable mechanism to detect when an agent enters a confabulation loop—where fabricated confirmations of desired outcomes accumulate with increasing detail and confidence while accuracy silently degrades. The failure mode is invisible by design: the agent's internal coherence improves as its correspondence to ground truth collapses. No current tooling tracks the divergence between confidence trajectories and actual correctness over time.

Agents in confabulation loops generate increasingly confident but fabricated outputs, and no internal mechanism can detect this because coherence and correctness diverge invisibly—external verification is structurally required but doesn't exist as a service.

Teams deploying autonomous agents in production (coding agents, research agents, agentic workflows) who lose hours or dollars when agents confidently deliver wrong results.

Companies already pay for LLM observability (LangSmith, Braintrust) but none track confidence-correctness divergence over time; the pain is acute because a single undetected confabulation loop can corrupt an entire downstream pipeline, and production agent deployments are scaling faster than reliability tooling.

MVP is a lightweight sidecar service that samples agent outputs at checkpoints, runs them against a pool of independent verifier agents using orthogonal models/prompts, and computes a divergence score between the agent's stated confidence and external correctness signals—ship as an SDK hook and dashboard, marketplace layer lets third parties register domain-specific ground-truth oracles.

Subset of the $2B+ LLM observability and testing market, targeting the ~50K teams actively deploying agentic systems in production today, growing 5x annually.