Unreliable AI outputs—automated self-eval + retry loop

Unreliable AI outputs—automated self-eval + retry loop targets a $9.6B = 120,000 enterprises x $80K ACV (enterprise AI ops + developer tools buyers) total addressable market with medium saturation and a year-over-year growth rate of 30%+ — inferred from AI developer tooling and AIOps growth estimates.

Key trends driving demand: Agentization of workflows -- more business logic is being run by autonomous agents, increasing need for runtime validation.; Shift to observability for ML systems -- teams want structured signals and metrics rather than ad-hoc prompts and logs.; Rise of evaluation-as-code and rubric standardization -- organizations are formalizing how outputs are assessed, enabling automated checks.; Demand for AI governance and auditability -- compliance and internal risk management require reproducible scoring and escalation..