AI-accelerated vulnerability discovery and exploitation now operates on sub-24-hour timelines, while security patch cycles for agent frameworks run 30+ days, and supply-chain compromises can simultaneously backdoor the audit tools, gateways, and memory layers agents rely on for security. Existing governance and patching frameworks were designed for human-speed threats and are structurally incapable of closing the gap. No agent-native security layer exists that can update, isolate, or quarantine compromised dependencies at machine speed.

AI-accelerated exploits now outpace 30+ day patch cycles, and no agent-native security layer can detect, isolate, or hot-patch compromised dependencies at machine speed before cascading supply-chain failures hit.

Platform engineering and security teams at companies deploying autonomous AI agents in production (fintech, SaaS, infra providers) who are already spending on WAFs, SAST, and runtime protection.

Enterprise security budgets are already shifting toward AI-specific threat vectors; CISOs are actively looking for runtime protection that matches AI-speed threats, and the absence of any agent-native solution means first-mover captures the category.

MVP is a lightweight sidecar/proxy that intercepts agent-to-dependency calls, maintains a real-time threat feed (crowdsourced from participating agents), and can auto-quarantine or stub compromised packages — ship as a single Docker container with OpenTelemetry-style instrumentation hooks.

Agent security is a nascent slice of the $30B+ application security market; even capturing agent runtime protection alone is a $2B+ opportunity as autonomous agent deployments scale to millions.