SREGym: High-Fidelity Benchmark for AI Site Reliability Engineering Agents
SREGym is a new benchmark for evaluating AI agents in Site Reliability Engineering (SRE). It provides a live system environment built on real-world cloud-native stacks, simulating high-fidelity failure scenarios through fault injectors. The benchmark models production complexity with faults at various layers, ambient noises, and failure modes like metastable and correlated failures. SREGym is modular and extensible, currently including 90 realistic SRE problems. It was used to evaluate frontier AI agents, though specific results are not detailed in the abstract. The work was announced on arXiv with ID 2605.07161.
Key facts
- SREGym is a benchmark for AI SRE agents.
- It uses a live system environment based on real-world cloud-native stacks.
- Failure scenarios are simulated via fault injectors.
- It models faults at different layers, ambient noises, and diverse failure modes.
- Includes 90 realistic SRE problems.
- The benchmark is modular and extensible.
- Used to evaluate frontier agents.
- Announced on arXiv with ID 2605.07161.
Entities
Institutions
- arXiv