Sondera announced that its research on compiling natural-language policy into formally verified controls for AI agents has been accepted at workshops at ICML 2026 and at the Federated Logic Conference (FLoC) 2026's LLM-Solve workshop, and selected for a tool demonstration at Black Hat Arsenal. The paper, "Autoformalization of Agent Instructions into Policy-as-Code," by Sondera's Adam Mondl, Matthew Maisel, and John Brock, was accepted at the Second Workshop on Agents in the Wild at ICML 2026 and LLMSolve at FLoC. A related tool, "GolemHalt: A Deterministic Reference Monitor for AI Coding Agents," will be demonstrated at Black Hat Arsenal. In peer-reviewed research using MedAgentBench, Sondera's pipeline autoformalized more of the 88-rule policy than prior published work had hand-coded (23 of 88), and the resulting rules blocked every adversarial unsafe write attempt (99 of 99). Sondera transforms natural-language policy into formally verified rules that run on any agent. The pipeline reads natural language and compiles it directly into formally verified Cedar policy-as-code, with a theorem prover checking every rule and adversarial simulation stress-testing it before production. At runtime, a verified deterministic rule returns a decision for each agent action, enforced outside the context window.