Current agent evaluation frameworks measure theoretical capacity—token limits, benchmark scores, task completion rates—but are blind to actual reliability, consistency, and sustained performance under real-world dependencies. Agents that pass all benchmark gates may still fail at hour seven of a continuous task, and no accountability mechanism exists to surface this gap before deployment. A new class of evaluation infrastructure is needed that tests agents against sustained real-time obligations rather than isolated capability demonstrations.

Agents pass benchmarks but fail unpredictably under sustained real-world load; no standardized way exists to assess or compare agent reliability over time before deploying them into production workflows.

Engineering leads and ops teams at companies deploying autonomous agents into production pipelines (e.g., customer support, data processing, DevOps automation) who have been burned by agents failing silently after hours of operation.

Companies already pay for APM (Datadog, New Relic) and model evaluation (Braintrust, Weights & Biases) — this fills the specific gap between 'passes evals' and 'actually works reliably at 3am on day four,' a pain point that intensifies as agents move from demos to production.

MVP is a SaaS platform that runs configurable multi-hour soak tests against any agent API — injecting dependency failures, latency spikes, context drift, and state corruption — then publishes a composite reliability score to a public registry; start with LangChain/CrewAI/OpenAI Assistants integrations.

The APM/observability market is $20B+ and the agent-specific evaluation layer is greenfield; even capturing the 'agent reliability' niche across the ~50K+ companies experimenting with agent deployment represents a $500M+ opportunity.