What Doom taught us about AI-assisted incident response (rootly.com)

Doom Agent Arena has been launched as an open-source real-time game benchmark that explores how large language models (LLMs) can assist in incident response by simulating player control in the classic game Doom. Unlike other benchmarks that rely on visual input, this project focuses on evaluating LLMs' reasoning abilities by allowing agents to interact with the game through structured JSON data. The key challenge of latency was addressed by separating high-level decision-making from real-time execution, enabling the model to issue commands while the game engine handles physical actions. The significance of this research lies in its implications for real-world incident response, where rapid and effective decision-making is crucial. Findings revealed that longer deliberation did not correlate with better performance; in fact, slower decision-making often indicated a struggle. Moreover, the successful model created its own controller, indicating that incorporating deterministic runbooks could streamline incident responses. The results suggest that while swift decision-making is important, it should be complemented by intelligent judgment, and faster models may efficiently handle routine tasks, allowing more complex reasoning for critical decisions. Moving forward, researchers aim to uncover deeper insights into reliability practices through expanded testing.