Your Agent Has a Genome: Sequence-Level Behavioral Analysis and Runtime Governance of LLM-Powered Autonomous Agents
Sidi Deng
Why It Matters
What makes this one worth your time
Understanding and improving the behavior of LLM-powered agents can lead to more efficient and successful autonomous systems, which is crucial for their deployment in real-world applications.
A novel framework for analyzing and governing LLM-powered agents' behavior using genomic sequence analysis techniques.
Summary
The paper introduces Base Sequence Analysis, a framework for encoding the behavior of LLM-powered autonomous agents into symbolic sequences, and applies genomic sequence analysis techniques to identify behavioral patterns affecting agent success. It proposes a runtime intervention system, Governor, which improves task success rates and reduces token consumption, and validates the framework's generality across different systems.
Key contributions
- Introduction of Base Sequence Analysis for encoding agent behavior.
- Development of the Governor system for runtime intervention, improving task success rates and reducing token consumption.
- Validation of the framework's generality across different agent systems.
Notable insights
- The use of genomic sequence analysis techniques like n-gram pattern mining and Markov transition matrices to analyze agent behavior is innovative.
- Identifying specific behavioral patterns that negatively impact success rates provides actionable insights for improving agent performance.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2606.15579v1 Announce Type: new Abstract: We propose Base Sequence Analysis, a framework that encodes the runtime behavior of LLM-powered autonomous agents into compact symbolic sequences using a four-letter alphabet: X (Explore), E (Execute), P (Plan), and V (Verify). Drawing an analogy to genomic sequence analysis, we apply n-gram pattern mining, Markov transition matrices, and point-biserial correlation to 347 real-world execution traces collected from a production ReAct agent system over 8 days. Our analysis reveals that (1) the trigram P-X-P is the only statistically significant high-risk pattern, lowering success rate by 10.4%; (2) P-ratio is the strongest negative predictor of success (r=-0.256, p V transition probability is only 2.1%, indicating a systemic verification deficit. Based on these findings, we design Governor, a three-layer runtime intervention system comprising a rule engine, a statistical accumulator, and a chi-square-based threshold adaptor. In a natural before/after deployment evaluation (N=101 vs. N=246), Governor achieves a +6.2% absolute increase in task success rate while simultaneously reducing average token consumption by 44%. To validate cross-system generality, we apply the XEPV encoding to 2,000 public SWE-agent trajectories on SWE-bench, confirming that exploration spirals and the E->V verification deficit replicate in an independent system. We outline six research directions including base sequence language models, cross-agent behavioral fingerprinting, and reward shaping, and release an open-source toolkit for reproducibility.