Evaluation of AI Agent's Autonomous Troubleshooting Capability: Practice of Sandboxed Engineering Testing Framework

This article explores how to build a high-difficulty AI agent evaluation system through a sandboxed testing environment, enabling large language models to demonstrate autonomous diagnosis and repair capabilities in real Linux terminal scenarios. This framework addresses the problem that traditional benchmark tests struggle to evaluate autonomous decision-making capabilities in dynamic and complex environments, providing a feasible path for the engineering evaluation of AI agents.

With the improvement of LLM capabilities, AI agents are evolving towards autonomous systems for complex engineering tasks, but traditional benchmark tests are difficult to evaluate their performance in dynamic environments. The core objective of this project is to develop a sandboxed testing system that requires agents to complete tasks such as environment perception, fault diagnosis, solution implementation, and persistent repair in a Linux terminal, closely simulating real DevOps scenarios.

Sandboxing is a key feature of the framework: ensuring security through container isolation to prevent destructive operations from affecting the host machine; starting each test from a brand-new environment to improve repeatability; supporting parallel testing to enhance efficiency; and enabling state snapshots and rollbacks to facilitate debugging of the agent's decision-making path.

Scenario design includes elements such as multi-level fault injection (chain-reaction faults), incomplete information (requiring multiple methods to collect clues), time and resource constraints (simulating real-scenario pressure), and persistent verification (restart/boundary testing to ensure robustness).

The evaluation system covers dimensions such as diagnostic accuracy (root cause localization logic), repair effectiveness (elegance of solutions and no side effects), degree of autonomy (need for human intervention), efficiency metrics (time/number of commands/resource consumption), and security and compliance (behavior boundary checks).

This framework marks the transition of AI agent evaluation from academia to engineering: evaluations should be close to real scenarios; autonomous capability is a core differentiating factor; standardized testing environments ensure repeatability and comparability, facilitating technology selection.

Future directions include expanding multi-domain scenario libraries, building automated evaluation pipelines (integrated with CI/CD), promoting community collaboration and standardization, and forming industry consensus to facilitate horizontal comparison of agent capabilities.