Semantic Invariance in Agentic AI

ABSTRACT

This paper investigates semantic invariance within agentic AI systems where reasoning agents interact with complex environments and external tools. The author explores how semantic drift during multi-step reasoning can lead to inconsistent actions or unstable behavior. A framework is proposed to maintain invariant semantic grounding across reasoning iterations and tool invocations. The work provides insights into improving reliability and interpretability of agent-based reasoning pipelines.