New Framework Analyzes and Governs LLM Agent Behavior at Runtime

A novel framework called Base Sequence Analysis has been developed to understand and govern the runtime behavior of LLM-powered autonomous agents. This approach translates complex agent actions into compact symbolic sequences using a four-letter alphabet: Explore (X), Execute (E), Plan (P), and Verify (V). This "genomic" representation allows for detailed behavioral analysis. Applying techniques like n-gram pattern mining and Markov transition matrices to real-world execution traces, the analysis revealed critical insights. For instance, the trigram P-X-P was identified as a statistically significant high-risk pattern, leading to a 10.4% reduction in success rates. Furthermore, a systemic "verification deficit" was observed, with a very low E->V transition probability, indicating agents often execute without sufficient verification. Based on these findings, a three-layer runtime intervention system named "Governor" was designed. This system, comprising a rule engine, statistical accumulator, and a chi-square-based threshold adaptor, was deployed and demonstrated a 6.2% absolute increase in task success rate while simultaneously cutting average token consumption by 44%. The generalizability of the XEPV encoding was further validated on an independent system, SWE-agent, confirming similar behavioral patterns and deficits.