An agent governance platform for enterprises running many production agents
A composable control layer that lets a company give AI agents real autonomy in regulated environments without locking everything down: an agent registry that makes every agent visible, risk scoring so reviews scale with danger, purpose-declared tool entitlements, shared evals, and a runtime governed by classifiers instead of blanket human approval.
online B2B Deep Tech / Infrastructure
From AI Engineer — AI Engineer NYC Livestream | Bridgewater, Anthropic, Two Sigma, Arize, Coatue, Nous, Gen. Intuition at 50:33
Problem: Non-deterministic agents are given autonomy over consequential systems (like the bank software that accidentally sent $900 million instead of $8 million in the Revlon/Citi incident). Blanket lockdown kills innovation, wide-open access is unsuitable for regulated firms, and policy documents in prompts aren't enforceable at runtime; meanwhile human-approves-every-action collapses into review fatigue.
For: Highly regulated enterprises (banks, hedge funds, financial infrastructure) whose teams are spawning agents everywhere with no one able to answer who owns an agent, what it can touch, or what it should and shouldn't do.
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Examples
- Two Sigma's internal five-control agent governance architecture: agent registry, risk-based review, purpose-declared tool entitlements, shared evals, runtime classification.
Other takes on AI security infrastructure
- Agentic consent governance layer for autonomous AI systems
- Enterprise agentic last-mile identity and access control layer
- Identity-based security infrastructure for multi-agent AI systems
- AI-agent security and control infrastructure for enterprises
- A guardrail tool that enforces strict test-driven development and other engineering rules for AI coding agents
- Privacy-first document preprocessing layer for AI coding agents