NetConfArena is presented, an executable benchmark for evaluating LLM agents in closed-loop network configuration, and its findings suggest two future directions: using validated trajectories as supervision signals to improve foundation models, and designing harness mechanisms that make agent execution more reliable and accountable.
Abstract
Large language model (LLM) agents are increasingly attractive for automating network configuration, yet their reliability and failure patterns are poorly understood. An essential prerequisite is to assess such agents in a realistic but risk-free environment. Existing benchmarks, however, fall short: they often treat configuration as static command generation or rely on overly simplified settings. Such evaluations understate the core challenges of network configuration, where correctness requires reasoning about protocol complexity and topology dependence. We present NetConfArena, an executable benchmark for evaluating LLM agents in closed-loop network configuration. NetConfArena places agents in emulated multi-device networks, provides a standardized and compact action interface for task execution, and evaluates the resulting network behavior with hidden task-specific executable test cases. The benchmark relies on an LLM-assisted, emulation-grounded pipeline, which converts human-oriented network materials into reusable parameterized task templates. We evaluate representative LLM agents on 480 task instances instantiated from 96 protocol-focused task templates, yielding 3840 execution trajectories, and show that failures are not limited to command errors. The failures also reveal gaps in task-specification adherence and robust planning and execution. These findings suggest two future directions: using validated trajectories as supervision signals to improve foundation models, and designing harness mechanisms that make agent execution more reliable and accountable.
Multi-agent systems built on large language models (LLMs) are increasingly deployed for complex tasks requiring autonomous planning, tool use, and inter-agent coordination. However, the non-deterministic nature of LLM outputs and the emergent behavior arising from agent interactions render traditional test oracles ineffective, creating a critical gap in quality assurance for agentic AI. This work introduces MORPHAGENT, a framework designed to address the oracle problem in multi-agent LLM systems through trace-based behavioral analysis. Our contributions are threefold: (1) goal-preservation relations that verify consistent goal achievement under input perturbations, (2) coordination-consistency relations that validate inter-agent delegation and communication patterns under agent substitution and reordering, and (3) tool-use integrity relations that ensure semantic equivalence of tool invocation sequences under prompt paraphrasing. MorphAgent instruments agent execution to capture structured traces comprising planning steps, tool calls, message exchanges, and final outputs, then systematically applies metamorphic transformations and checks behavioral invariants without requiring ground-truth oracles. We evaluate the framework on four multi-agent benchmarks spanning code generation, research synthesis, customer service, and data analysis tasks, encompassing 2,840 source-followup execution pairs across three LLM backends. Results show that MORPHAGENT detects 82.0% of seeded behavioral faults, including 90.3% of coordination failures and 81.7% of goal-deviation faults, while maintaining a false positive rate of 6.1%. The framework uncovers 14 previously unreported behavioral anomalies in established multi-agent frameworks, demonstrating its practical utility for assuring agentic AI reliability. These results suggest that trace-based metamorphic testing can serve as a practical foundation for reliable validation of emerging agentic AI systems.
Gopalakrishnan Marimuthu· International Conference on...· 0 citations
LLM-based autonomous agents fail in ways that existing observability infrastructure cannot detect. OpenTelemetry’s GenAI semantic conventions cover LLM invocation and tool execution but leave five critical agent orchestration phases—planning, reasoning, safety monitoring, inter-agent delegation, and memory management—without span-level representation. We present AgentTelemetry, an open-source benchmark suite and toolkit for evaluating fault detection in agent systems. The benchmark defines (1) a taxonomy of 14 fault types mapped to 9 agent-specific span kinds, (2) a controlled evaluation harness of 490 fault-detection cells (14 faults × 5 observability conditions × 7 frameworks; enumerated as 2,940 raw configurations across 6 mock-LLM seeds), and (3) a pip-installable library (3,700+ LOC, 78 tests) with adapters for seven frameworks. On the controlled benchmark, the full span taxonomy achieves a Fault Detection Rate (FDR) of 1.000—an upper bound confirming structural completeness—compared to 0.429 for vanilla OpenTelemetry and OTel+GenAI. An ablation study proves all nine span kinds are necessary: removing any one makes at least one fault type undetectable. A case study on 112 SWE-bench Lite instances reveals that 84/112 agent runs (75%) exhausted the 8-iteration limit and are classified as reasoning loops by structural pattern (a definitional partition of the failed-trace population, not a sampling estimate)—a failure mode invisible to vanilla OTel—and a telemetry-guided intervention improves the patch rate by +12.5 pp over a matched control (Fisher’s exact p=0.53, two-sided; demonstrative not statistically significant at n=24). All code, data, and benchmark configurations are open-source for reproducibility.
This work presents ComponentBench, a benchmark and diagnostic pipeline for component-level evaluation of computer-use agents on modern web UIs, and introduces a scalable pipeline for auditing realized structural difficulty after implementation and synthesizing structured failure analyses across tasks and component families.
Tianchen Guan, Xinlei Lin, Royce Cheng-Yue et al.· 0 citations
DynamicMCPBench is presented, a reusable framework rather than a fixed dataset that turns benchmark construction into something practitioners can rerun on their own servers and models, while exposing a consistent inability of current agents to handle long, multi-step agentic tasks.
Jerzy Kamiński, Ilya Galyukshev, Artem Kuznetsov et al.· 0 citations
Penetration testing is essential yet resource-intensive. Although large language models (LLMs) show promise for automating security auditing, existing agents mainly execute end-to-end workflows in simplified linear scenarios. Real-world black-box testing is fundamentally nonlinear: the attack graph is initially unknown and must be incrementally inferred from environmental feedback. Observations may reveal multiple attack branches, failures are often ambiguous, and critical clues may span long action horizons. Existing agents therefore tend to become trapped in depth-first exploration, misdiagnose failures, and forget prior evidence. We present MazeRunner, an autonomous penetration testing system built on a three-agent task-and-clue orchestration framework. It separates global orchestration, context-intensive execution, and failure-oriented review while persistently maintaining task states and environmental evidence. This design supports action revision, prerequisite recovery, branch switching, and long-range clue correlation. We evaluate MazeRunner on 10 recently released HTB targets, limiting each system-target run to 20 million LLM tokens and preventing target-specific solution leakage. With Claude Sonnet 4.5, MazeRunner completes 47.7% of annotated subtasks, compared with 36.2% for PentestGPT-V2 and 34.2% for Claude Code. It achieves user-level or higher access on six targets, including root access on two; each same-model baseline reaches user-level access on only two targets and never obtains root access. Execution-trace analysis further shows that MazeRunner explores more attack branches and acquires shells more efficiently.
Zhenyuan Li, Yi Jiang, Junjie Cheng et al.· 0 citations
A Network Experimentation Harness for emulator-backed network experiments, helping LLM agents operate across these stateful workflows by pairing a semantic action interface with reusable experimentation skills to handle sequencing, timing, and verification that a careful experimenter would perform by hand.
Chenguang Du, Chang Liu, Lei Zhang et al.· Asia-Pacific Workshop on Net...· 0 citations