Toward Efficient LLM Agents for Emulator-Based Network Experiment Automation

Abstract

Agent-driven scientific experimentation is emerging across domains such as chemistry, biology, and materials, yet each tool class imposes its own execution discipline. Network experimentation requires more than one-shot topology or configuration synthesis: an experimenter must plan a task, operate a live and evolving network, interpret feedback, refine intermediate state, and validate the resulting behavior. This poster presents a Network Experimentation Harness for emulator-backed network experiments, helping LLM agents operate across these stateful workflows. The Harness pairs a semantic action interface with reusable experimentation skills to handle sequencing, timing, and verification that a careful experimenter would perform by hand. A preliminary study on GNS3-based network protocol experiments shows that this approach reduces wall-clock time by 47% and 37%, and token use by 81% and 76%, on average versus raw GNS3 access and a Python wrapper (GNS3Fy), respectively.