Formally synthesized agents for ensuring safety and constraint compliance of AI agents
SymbolicML builds formally synthesized shielding and control technologies that keep AI agents within provable safety, policy, and optimality constraints so violating behaviors cannot occur. Our systems combine program synthesis, symbolic reasoning, and temporal logic to provide dependable oversight across agent architectures, whether the underlying model is an LLM, a reinforcement learning policy, or a deep learning system. This foundation enables dependable decision-making in high-stakes domains, with applications in robotics, autonomous drones, finance, real estate, and law.