Agentic Alignment: Inverse Reinforcement Learning from Swarm Behavior

ABSTRACT

Aligning multi-agent systems via traditional human feedback is intractable due to the sheer volume and speed of agent-to-agent interactions. We introduce a novel alignment framework utilizing Inverse Reinforcement Learning (IRL) applied directly to swarm behavior. Instead of grading individual prompt outputs, our system observes the emergent macro-behaviors of an Agentic AI economy and mathematically infers the underlying reward functions the agents have implicitly constructed. By identifying and automatically dampening 'misaligned utility functions' (such as recursive resource hoarding), our framework provides the first scalable method for governing the safety of trillion-parameter agentic networks.