SREGym treats the Site Reliability Engineering (SRE) incident management process as a training ground for AI agents to form core system intelligence capabilities. During an incident, SREs must diagnose root causes in complex distributed systems, mitigate failures and solve the root cause of the failure. This makes SRE a rich, realistic testbed for AI: agents must reason across system boundaries, interpret noisy signals (logs, metrics, traces), and execute safe remediation actions, yet we believe they can be trained to reliably assist or autonomously handle critical incidents.
Site Reliability Engineering has become the standard for operating large-scale software systems. Despite best practices, the practical work of incident response remains stressful and high-stakes. To alleviate this burden, we envision automated SRE agents that execute reliable diagnosis and mitigation. Startups, cloud providers and The capability of the agents in incident response can also show the critical capabilities of agents' understanding of the system.
SREGym focuses on the core phases of the incident lifecycle, mirroring the critical tasks performed by human Site Reliability Engineers during production outages:
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Diagnosis (Root Cause Analysis). In a real-world incident, SREs must rapidly identify why a system is failing under pressure. This involves navigating complex distributed systems, correlating noisy signals (logs, metrics, traces) across the stack, and verifying hypotheses to pinpoint the underlying fault (e.g., code bug, configuration drift, or infrastructure failure).
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Mitigation. Once the issue is understood (or sometimes even before, to stop bleeding), SREs must determine how to restore service health. This requires executing safe, decisive actions—such as rolling back deployments, draining traffic, or restarting services—while carefully managing the risk of collateral damage to ensure the system returns to a healthy state and meets SLAs.
Reliability engineering is obstructed by multiple factors that make it a formidable AI challenge:
- Complexity and Scale: Modern microservice architectures generate vast amounts of data. Finding a signal in the noise of terabytes of logs and metrics is non-trivial. SREGym provides a noise generator to evaluate the agents' ability to handle noise.
- Partial Observability: Failures often occur in "blind spots" where instrumentation is missing or misleading (e.g., silent failures, heisenbugs). Text-based RCA problems, the agents are exposed to the problems directly, but in the real scenario SREGym creates, the agents need to reason the system appearance from the logs and metrics and identify the problems.
- Fail-slow: Some failures do not cause immediate system failure, but they can cause the system to degrade over time. SREGym includes fail-slow faults to evaluate the agents' ability to find and solve these sorts of problems.
- Time-to-mitigate: SREGym enables the evaluation of the agents' efficiency in mitigating the faults, which is a critical metric for SRE.