Your Agent Has a Genome: Sequence-Level Behavioral Analysis and Runtime Governance of LLM-Powered Autonomous Agents

The article introduces Base Sequence Analysis, a framework that encodes the runtime behavior of LLM-powered autonomous agents into symbolic sequences, utilizing a four-letter alphabet (X, E, P, V). The study analyzes 347 execution traces from a ReAct agent system, identifying significant behavioral patterns and developing Governor, a three-layer runtime intervention system that improves task success rates by 6.2% while reducing token consumption by 44%. This research provides insights into agent behavior, proposes avenues for further exploration in sequence language models, and includes an open-source toolkit for reproducibility, which is crucial for practitioners aiming to enhance LLM performance and governance.