SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios

SREGym is a new open-source benchmark platform that enables realistic evaluation of AI agents designed to diagnose and fix failures in production systems. The framework simulates high-fidelity failure scenarios across cloud-native stacks and currently includes 90 SRE problems, revealing significant performance variations among frontier AI models.

SREGym addresses a critical gap in AI infrastructure evaluation by providing the first genuinely realistic benchmark for agentic Site Reliability Engineering. Traditional SRE benchmarks have relied on oversimplified tasks that don't reflect the complexity of production environments, making it difficult to assess whether AI agents can handle real-world reliability challenges. This new platform introduces fault injection across multiple system layers, ambient noise simulation, and complex failure modes like metastable and correlated failures—conditions that distinguish production systems from lab environments.

The development of SREGym reflects the growing adoption of AI agents in DevOps and infrastructure management. As organizations increasingly deploy autonomous systems to handle incident response and system diagnostics, the need for rigorous evaluation frameworks becomes paramount. The finding that frontier AI models show performance variations of up to 40% across different failure types suggests current models have significant blind spots in specific reliability domains.

For the AI infrastructure industry, SREGym provides both opportunity and sobering reality. The benchmark enables more honest assessment of AI capabilities in critical infrastructure roles, which is essential before widespread production deployment. For enterprises considering AI-driven SRE solutions, these results indicate careful vendor evaluation is necessary—model capability isn't uniform across failure scenarios. The open-source, actively maintained design positions SREGym as a standard evaluation tool, potentially influencing which AI models gain adoption in enterprise infrastructure management and driving improvements in frontier model reliability capabilities.

• →SREGym provides the first high-fidelity benchmark for evaluating AI agents in production system reliability tasks with 90 realistic scenarios.
• →Frontier AI models show up to 40% performance variation across different failure types, indicating uneven capabilities in infrastructure management.
• →The framework simulates complex production conditions including multi-layer faults, noise, and correlated failures that traditional benchmarks lack.
• →Open-source maintenance and active use by researchers establishes SREGym as a potential industry standard for SRE agent evaluation.
• →Results suggest enterprises must carefully evaluate specific AI model capabilities for their infrastructure before deployment in critical systems.