AI-generated code ships fast, but runtime control hasn’t kept up

AI has significantly accelerated the pace of software development, allowing for tasks like code scaffolding and test generation to be completed in minutes, but it hasn't fundamentally changed how production control is managed, according to the LaunchDarkly 2026 AI Control Gap Report. While 94% of engineering leaders note the increased speed of code generation, 91% express heightened caution about deploying changes due to the persistent challenge of maintaining production reliability. This disconnect has led to frequent rollbacks and hotfixes, with only a small fraction of teams able to resolve issues swiftly. AI-generated code, more unpredictable than human-written code, exacerbates these challenges by introducing new runtime risks. Although many teams employ runtime safety mechanisms such as feature flags and real-time monitoring, inconsistent implementation and lack of integration undermine their effectiveness. Only 15% of teams successfully combine rapid deployment with incident control by prioritizing runtime control, enabling them to adapt feature behavior post-deployment without rebuilding or redeploying. This approach allows for safer, more frequent releases and quicker issue resolution, enhancing both efficiency and customer experience. As AI continues to complicate software development, embedding runtime controls into delivery workflows becomes essential for maintaining velocity and stability, with the most successful teams adapting live systems proactively rather than reactively.