The Problem AI agents are moving from answering questions to taking actions — calling APIs, querying databases, executing code, managing memory. The security surface has shifted from "what the model says" to "what the agent does." Most guardrail solutions address the first problem. They filter content. They detect prompt injection. They moderate output. These are necessary but insufficient. The gap: who decides what the agent is allowed to do once it's been talked into doing it? Tool authorization. Memory governance. Cost limits. Audit evidence. These aren't content safety problems — they're governance problems. And they require a different architecture. What We Built TealTiger v1.2 is a deterministic governance engine for AI agents. It evaluates every agent action against policy — in parallel, at runtime, with no LLM in the decision path. The key design constraint: same input + same policy = same decision, every time. No probabilistic scoring. No model inference.…