Agile’s focus on delivering working software frequently has evolved into continuous integration/continuous delivery practices. AI is now pushing this boundary further toward what we might call “continuous creation.” When code generation approaches real-time, the limiting factor isn’t producing code but verifying it. AI offers solutions here as well—automated testing, security scanning, and quality analysis can be AI-enhanced. AI agents can write unit tests for new code and help create end-to-end tests, improving quality guarantees. The most successful teams will master this balance between acceleration and validation, exploring more ideas, failing faster, and converging on optimal solutions more quickly—all while maintaining high quality. These transformations create opportunities to streamline traditional Scrum processes. Teams can allocate a higher percentage of their sprint to spontaneous improvements as implementing features and bug fixes with AI may be faster than the overhead of including them in sprint planning. For architecture reviews, AI can serve as your first wave of feedback—a mental sparring partner to develop ideas before presenting to a committee. The AI-written summary can be shared asynchronously, often eliminating the need for formal meetings altogether. Retrospectives should now include discussions about AI usage. The improved individual productivity allows organizations to streamline overhead processes, leading to further increases in velocity. Teams can tackle larger, more complex problem spaces, and projects that previously required multiple teams can often be handled by a single team. Cross-team dependencies—a perennial challenge in scaled agile—diminish significantly. What’s most remarkable about AI’s impact is how it reinforces rather than replaces agile’s core values.