Datadog, a global provider of observability solutions for complex infrastructures, has incorporated artificial intelligence into its code‑review process to reduce the risk of production incidents. The move follows a growing need to balance rapid deployment with operational stability, a challenge that has intensified as the company’s engineering teams expand.
Background
Reliability is a core requirement for Datadog, whose platform is used by customers to diagnose failures in distributed systems. Because the platform must detect root causes before software reaches production, the company relies heavily on code review as the primary gatekeeper for quality. However, as teams grow, maintaining deep contextual knowledge of the entire codebase through manual review becomes unsustainable. The engineering leadership recognized that systemic risks often escape human detection at scale, prompting a search for automated solutions.
AI Implementation
Datadog’s AI Development Experience (AI DevX) team integrated OpenAI’s Codex model into its workflow. The integration was applied to one of the company’s most active repositories, enabling the AI to review every pull request automatically. Unlike traditional static‑analysis tools, the AI compares the developer’s stated intent with the actual code changes and executes tests to validate behavior. This approach allows the system to reason about how a change might affect interconnected services, rather than merely flagging style violations.
Testing and Validation
To demonstrate the tool’s value beyond theoretical efficiency, the team created an incident replay harness that ran the AI against historical outages. The harness reconstructed pull requests that had previously caused incidents and evaluated whether the AI would have flagged the issues missed by human reviewers. The results showed that the AI identified more than ten cases, representing approximately 22 % of the examined incidents, where its feedback could have prevented the error. These cases had already bypassed human review, illustrating the AI’s ability to surface risks invisible to engineers at the time.
Impact on Engineering Culture
Since its deployment to over 1,000 engineers, the AI has influenced the organization’s code‑review culture. Engineers report that the system consistently flags issues that are not obvious from the immediate code diff, such as missing test coverage in cross‑service coupling and interactions with modules that the developer did not touch directly. The depth of analysis has shifted human reviewers’ focus from catching bugs to evaluating architecture and design. Brad Carter, leader of the AI DevX team, said, “Preventing incidents is far more compelling at our scale.” He added that the AI acts as a partner that handles the cognitive load of cross‑service interactions, rather than replacing the human element.
Strategic Implications
The Datadog case study illustrates a broader shift in how code review is defined within enterprise environments. It is no longer viewed merely as a checkpoint for error detection or a metric for cycle time; instead, it functions as a core reliability system. By surfacing risks that exceed individual context, the technology supports a strategy where confidence in shipping code scales alongside the team. This aligns with Datadog’s leadership priorities, which see reliability as a fundamental component of customer trust. Carter noted, “We are the platform companies rely on when everything else is breaking. Preventing incidents strengthens the trust our customers place in us.” The successful integration suggests that the highest value of AI in the enterprise may lie in enforcing complex quality standards that protect the bottom line.
Future Outlook
Datadog plans to expand the AI code‑review system to additional repositories and to refine its incident replay harness for broader coverage. The company is also exploring ways to integrate the AI’s contextual insights into continuous integration pipelines, aiming to further reduce the incidence of production failures. As the organization continues to scale, the AI’s role as a reliability partner is expected to grow, potentially setting a new standard for code‑review practices in large, distributed engineering teams worldwide.
