AI agents need enforceable stop‑signs — runtime safety controls

Enterprises moving from single-use models to fleets of autonomous agents face a new class of safety gaps: agents can take cascading actions across systems and data, and existing mitigations—instruction tuning and static policies—do not provide enforceable, auditable stops at runtime. This problem affects large organizations in regulated industries and operations teams at scale; using conservative estimates there are roughly 500,000 enterprises that could spend on these controls, supporting a total addressable market we estimate near $95.0B and an average category spend of about $190K per year. You could build a runtime safety platform that provides enforceable “stop‑signs” and controls for agents: a low-latency policy engine that can interpose on agent decisions, cryptographically verifiable kill-switches and capability gating, tamper-evident audit trails, and lightweight agent-side SDKs and connectors for major orchestration layers and identity systems. Offerings would include a SaaS control plane with hybrid on‑prem enforcement nodes for sensitive environments, real-time telemetry and forensics, and certification tooling so customers can demonstrate compliance to auditors. The market dynamics make this timely—widespread agent adoption, increasing regulatory guidance, and a shift from static controls to runtime enforcement all push buyers toward solutions that can prove behavior in real time; our market and revenue scores (Market Score 95/100, Revenue Potential 90/100) reflect that. To stand out against medium competition you should emphasize provable enforceability and interoperability with existing tooling, build strong partnerships with cloud, orchestration, and security vendors, and be candid about the challenges: complex integrations, adversarial agent behavior, and immature standards will require time and focused investment to overcome.