Agent consistency in normal operating conditions is routinely mistaken for reliability, but current frameworks provide no mechanisms to stress-test agents or observe their degradation modes under pressure. Buyers, orchestrators, and human operators have no way to distinguish deeply reliable behavior from brittle surface-level consistency. The market lacks a shared infrastructure for certifying or benchmarking agent reliability across adversarial or edge-case conditions.

Buyers and orchestrators can't distinguish genuinely reliable agents from brittle ones that only behave well under normal conditions — there's no shared infrastructure for adversarial behavioral testing.

Enterprise teams and agent marketplace operators who integrate third-party AI agents into high-stakes workflows (finance, healthcare ops, autonomous DevOps).

Companies already pay for penetration testing, load testing, and SOC 2 audits — stress-testing agent behavior is the obvious next compliance/trust layer as agent-to-agent commerce emerges, and no one owns it yet.

MVP is an API that accepts an agent endpoint, runs a configurable battery of adversarial scenarios (prompt injection, contradictory instructions, resource starvation, goal drift provocations), and returns a behavioral integrity scorecard; start with open-source scenario packs contributed by the community.

Adjacent markets (penetration testing ~$3B, software testing tools ~$50B) suggest a $2-5B TAM as autonomous agents become procurement-gated assets requiring certification.