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

2026-03-05 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Expert, extended

Summary

SREGym is a new, high-fidelity benchmark designed to evaluate AI agents for Site Reliability Engineering (SRE) tasks in live production-like cloud-native environments. It addresses limitations of existing benchmarks by simulating complex failure scenarios, including a wide range of faults across hardware, OS, and application layers, various ambient noises, and diverse failure modes like metastable and correlated failures. Built as a modular, extensible framework, SREGym currently includes 90 realistic SRE problems and offers a unified programming interface for problem curation. Evaluations using SREGym on frontier agents like Stratus, Claude Code, and Codex, powered by models such as Sonnet-4.6, GPT-5.4, and Kimi K2.5, revealed significant performance variations, with up to 40% differences in end-to-end success rates depending on the failure type. The benchmark is open-source and actively maintained, providing a foundation for advancing agentic SRE technologies.

Key takeaway

Research Scientists developing AI SRE agents should prioritize improving agent capabilities in diagnosing and mitigating complex, high-fidelity failures, particularly those rooted in lower-level system stacks (OS, hardware) and involving compound or metastable failure modes. Your agents must move beyond greedy anomaly detection and develop a more comprehensive, coherent understanding of system interactions to effectively handle real-world production incidents, as current frontier agents show significant performance drops (up to 40%) in these challenging scenarios.

Key insights

SREGym provides a high-fidelity, live benchmark for AI SRE agents, revealing significant performance gaps in handling complex, real-world failures.

Principles

• Live environments are crucial for realistic SRE agent evaluation.
• Composability is key to scaling SRE problem scenarios.
• Avoid misusing chaos engineering tools for fault injection.

Method

SREGym defines problems as a four-tuple $(\mathcal{E},\mathcal{I},\mathcal{F},\mathcal{O})$, simulating faults and noises in a Kubernetes-based environment, and evaluating diagnosis and mitigation using an LLM-as-a-judge protocol and problem-specific oracles.

In practice

• Evaluate SRE agents against low-level stack and compound failures.
• Design agents to distinguish root causes from ambient noise.
• Implement self-validation to recover from incorrect diagnoses.

Topics

• AI SRE Agents
• SREGym Benchmark
• High-Fidelity Failure Scenarios
• Cloud-Native Systems
• Fault Injection

Code references

• SREGym/SREGym
• chaosblade-io/chaosblade
• grafana/loki
• open-telemetry/opentelemetry-demo
• Rootly-AI-Labs/SRE-skills-bench

Best for: Research Scientist, AI Scientist, MLOps Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.