When the Agile Manifesto was signed in 2001, its authors couldn’t have imagined how artificial intelligence would eventually reinforce and radically extend their principles. Two decades later, AI isn’t just changing how we write code—it’s transforming the fundamental nature of software development itself.

Agile methodologies emerged as a response to the rigid, documentation-heavy “waterfall” approach that dominated software development throughout the 1990s. The manifesto prioritized “individuals and interactions over processes and tools” and “responding to change over following a plan.” Today’s AI tools are enabling these human-centered principles to reach their full potential in ways that would have seemed like science fiction to the original signatories.

The Rise of the Cross-Functional Individual

Agile has long advocated for self-sufficient cross-functional teams—groups with all the skills needed to deliver features without external dependencies. But AI is taking this principle to its logical extreme: the cross-functional individual.

Engineers increasingly use AI to venture into unfamiliar parts of the codebase and stack that they previously lacked the knowledge or time to work with. The already growing demand for “Full Stack Engineers” is accelerating this convergence, as AI tools help developers bridge knowledge gaps and work across disciplines with unprecedented efficiency.

Embracing Change at Unprecedented Speed

“Responding to change over following a plan” has always been a core agile value. Traditional methodologies viewed change as costly and disruptive, while agile embraced it as inevitable and valuable. AI amplifies this principle dramatically by slashing the cost of change.

This reduced cost encourages more experimentation, more pivots, and ultimately better products. The “bravery” that agile encourages—the willingness to change direction based on new information—becomes significantly easier when the penalty for change is dramatically reduced. Features that once took sprints can be prototyped in hours, and user feedback can be incorporated almost immediately.

From Continuous Delivery to Continuous Creation

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.

Reimagining Agile Processes

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.

Consider adapting the personal productivity “2-minute rule” for AI-enhanced teams: “If it takes less than 15 minutes to correctly prompt an AI agent to implement something, do it immediately rather than putting that task through the entire backlog/planning process.” While the agent works, you can focus on your other activities. And if the agent fails, you can ask AI to create a user story for the backlog. With the right integrations, this approach dramatically increases efficiency.

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: Which team members pushed AI capabilities to new frontiers? Where did AI implementation attempts fail, and what could unblock progress? These conversations address not just how humans work together, but how humans and AI collaborate effectively. As you are building your individual and team AI skills, patterns, and solutions, you will be able to unlock the next level of productivity. 

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.

This transformation is particularly impactful for organizations using scaled frameworks like SAFe, LeSS, or Nexus. These frameworks were designed primarily to coordinate multiple teams working on shared products—a necessity that AI is making increasingly optional.

The Enduring Spirit of Agile

What’s most remarkable about AI’s impact is how it reinforces rather than replaces agile’s core values. The Manifesto’s focus on individuals, working software, customer collaboration, and responding to change remains as relevant as ever—perhaps more so.

The tools have changed dramatically, but the principles endure. The difference is that we now have unprecedented capabilities to fulfill those principles. The agile teams that thrive in this new environment will be those who best integrate these tools into human-centered development processes.

Twenty years ago, agile helped us escape the rigidity of waterfall development. Today, AI is helping us unlock the next level of human creativity in software development. The future isn’t just faster—it’s fundamentally more creative, more responsive, and more human than ever before.