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1 month ago
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Integrating AI into code review workflows allows engineering leaders to detect systemic risks that often evade human detection at scale.
For engineering leaders managing distributed systems, the trade-off between deployment speed and operational stability often defines the success of their platform. Datadog, a company responsible for the observability of complex infrastructures worldwide, operates under intense pressure to maintain this balance.
When a client’s systems fail, they rely on Datadog’s platform to diagnose the root cause—meaning reliability must be established well before software reaches a production environment.
Scaling this reliability is an operational challenge. Code review has traditionally acted as the primary gatekeeper, a high-stakes phase where senior engineers attempt to catch errors. However, as teams expand, relying on human reviewers to maintain deep contextual knowledge of the entire codebase becomes unsustainable.
To address this bottleneck, Datadog’s AI Development Experience (AI DevX) team integrated OpenAI’s Codex, aiming to automate the detection of risks that human reviewers frequently miss.
English (US) ·