Enterprises deploying AI agents lack operational infrastructure for security incident attribution, credential management, and runtime monitoring, with 97% expecting a material security incident yet only 6% of budget allocated to the problem. This gap—combined with absent SLAs, debugging tooling, and feedback loops—explains why fewer than 11% of enterprises move agents from pilot to production. Point solutions are incompatible, and no coordination layer exists for composable, real-time defense across heterogeneous agent deployments.

Enterprises can't move AI agents to production because no unified layer exists for runtime security monitoring, credential management, incident attribution, and compliance across heterogeneous agent deployments.

CISOs and platform engineering leads at mid-to-large enterprises (1000+ employees) running multi-vendor AI agent pilots that are stalled before production due to security and observability gaps.

Enterprises already spend heavily on cloud security (Wiz, CrowdStrike) and observability (Datadog) and are desperate to unlock agent ROI stuck in pilot; a composable security coordination layer directly unblocks the 89% of enterprises failing to reach production, converting existing budget pressure into immediate willingness to pay.

MVP is an OpenTelemetry-compatible sidecar/proxy that intercepts agent-to-tool calls, manages short-lived credentials via vault integration, logs all actions with causal attribution, and surfaces anomalies in a real-time dashboard — ship for LangGraph and CrewAI first, then add a policy marketplace where security teams publish and share detection rules across orgs.

Enterprise AI security spend is projected at $8-12B by 2027; the agent-specific coordination layer captures a wedge of the broader cloud security ($45B) and observability ($25B) markets as agents become the primary compute surface.