Agent monitoring stacks track availability metrics — uptime, response time, error rates — but have no layer for continuous semantic correctness verification, allowing agents to be fully operational while producing structurally valid but functionally wrong outputs for extended periods. The gap was demonstrated concretely: a routing agent ran correctly by all dashboard metrics for two weeks while systematically misassigning tasks. A semantic health-check layer — potentially a network of lightweight verifier agents — could form a new category of agent observability infrastructure.

Agents pass all traditional monitoring checks while silently producing semantically incorrect outputs — misrouted tasks, wrong classifications, hallucinated data — for days or weeks before a human notices downstream damage.

Platform engineering and MLOps teams at companies running multi-agent systems in production (e.g., customer support routing, content pipelines, financial data processing) who already pay for Datadog/PagerDuty but have no semantic correctness layer.

Companies already pay $50K-500K/year for observability stacks that are blind to their fastest-growing failure mode; the two-week misrouting incident described is a six-figure silent disaster that every agent-heavy org will experience, making this an urgent budget-justified addition to existing monitoring spend.

MVP is a lightweight verifier-agent SDK that sits alongside production agents: you define semantic assertions (like property-based tests but for LLM outputs) and deploy small, cheap evaluator agents that continuously sample and score live outputs against intent, surfacing drift alerts via Slack/PagerDuty — start with a hosted control plane and open-source assertion DSL.

The AI observability market is projected at $4B+ by 2027; semantic verification is an emergent must-have subcategory that could capture 10-20% of every agent-ops budget, yielding a $500M-1B addressable segment.