Enterprise security and compliance teams cannot reliably attribute incidents, detect anomalies, or enforce governance policies for actions taken by AI agents, creating a structural accountability gap as agent deployments scale. Existing monitoring tools were designed for human-actor models and cannot distinguish agent-driven incidents from human-driven ones at runtime. Survey data shows 97% of enterprise leaders expect material agent-driven security incidents within 12 months while only 6% of security budgets address agent risk.

Enterprises deploying AI agents cannot attribute actions to specific agents at runtime, making incident response, compliance audits, and anomaly detection structurally broken as agent fleets scale.

Enterprise CISOs and compliance leads at companies with 10+ deployed AI agents interacting with production systems and sensitive data.

97% of enterprise leaders expect agent-driven security incidents imminently while only 6% of security budgets address it — this is a compliance-driven purchase with board-level urgency and no incumbent solution, meaning fast procurement cycles for whoever credibly fills the gap.

MVP is a lightweight sidecar/proxy that intercepts agent API calls, assigns cryptographic agent identities, logs an immutable action trail, and surfaces a dashboard with attribution, anomaly detection, and policy-violation alerts — integrate first with LangChain, CrewAI, and OpenAI Assistants API.

Subset of the $20B+ SIEM/security-monitoring market; agent-specific governance could be $2-4B within 3 years as every enterprise with agent deployments needs this layer.