Enterprise deployments of AI agents lack systematic infrastructure for monitoring inter-agent communication, verifying trust boundaries, and enabling human intervention at runtime. Existing security frameworks treat agents like traditional software, missing the cascading, autonomous nature of agentic attack chains and governance failures. No established pattern exists for visibility, authorization, or shutdown control across multi-agent environments.

Enterprises deploying multi-agent systems have zero visibility into inter-agent authorization, trust propagation, and cascading failures — and no kill-switch when agents go off-rails at runtime.

Platform engineering and security teams at enterprises running multi-agent workflows (finance, healthcare, logistics) who face compliance mandates and board-level AI risk concerns.

Enterprises are blocked from production agent deployments by their own security and compliance teams; AgentGate is the missing approval gate that unblocks millions in stalled AI transformation budgets, similar to how HashiCorp Vault unlocked secrets management as a paid category.

MVP is a lightweight sidecar/proxy that intercepts agent-to-agent calls via standard protocols (OpenAI function calling, LangGraph edges, MCP), enforces policy-as-code authorization rules, logs full interaction traces, and exposes a real-time dashboard with human-in-the-loop circuit breakers — ship for one framework first (e.g., CrewAI or LangGraph).

Subset of the $15B+ cloud security and API gateway market, with every enterprise running agentic AI becoming a buyer — conservatively $2B+ TAM within 3 years as agent deployments scale.