Your Agent Has a Genome: Sequence-Level Behavioral Analysis and Runtime Governance of LLM-Powered Autonomous Agents
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 hig
The proliferation of LLM-powered autonomous agents in production environments necessitates robust methods for understanding, analyzing, and governing their emergent behaviors, making this research timely.
Sophisticated analysis of agentic behavior provides tools for managing complexity, enhancing reliability, and enabling governance of autonomous systems, which is crucial for their safe and effective deployment.
The ability to encode and analyze agent behavior with 'genomic' techniques enables a new level of diagnostics and control previously unavailable for complex LLM agents.
- · AI developers
- · Enterprise AI adopters
- · AI governance platforms
- · Cybersecurity firms
- · Ungoverned autonomous agent systems
- · Legacy AI monitoring tools
Improved debugging and optimization of LLM agents will accelerate their deployment across various industries.
The development of 'behavioral vaccines' or 'genetic edits' for agentic systems could emerge, allowing for proactive risk mitigation.
This could lead to a 'natural selection' environment for agent architectures, favoring those with predictable and governable 'genomes'.
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Read at arXiv cs.AI