Introducing Scout 1.0: A new SRE model powering Bits AI with frontier performance at a fraction of the cost

Datadog has introduced Scout 1.0, an AI model designed specifically to investigate production issues and power Bits Investigation, its AI SRE system. Built on GLM-5.3 Flash and post-trained through reinforcement learning using simulations based on historical incidents and telemetry without customer data, Scout is intended to form hypotheses, query operational data, identify likely root causes, and recommend recovery actions. Datadog reports that Scout’s medium-effort version achieves investigation quality comparable to several general-purpose frontier models at substantially lower estimated inference costs, while its higher-effort mode uses additional computation to improve accuracy on complex incidents. The company evaluated the model using its internal AlertEval benchmark, which incorporates real alerts, archived telemetry, and engineer-written root-cause explanations, as well as the third-party SREGym benchmark. Scout is currently being used by Datadog engineering teams and select preview customers, with plans to broaden failure simulations, improve efficiency and investigation quality, and eventually support autonomous remediation of incidents.

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