Since the Agile Manifesto emerged in 2001, software project management has been grounded in human interaction and collaboration among specialists

Frameworks like Scrum, Kanban, and Extreme Programming (XP) were created to optimize the productivity of multidisciplinary teams, ensuring alignment across development, design, and product. However, the landscape has shifted.

Artificial intelligence is no longer just an auxiliary tool, it is becoming an active agent in software development.

With the automation of repetitive tasks, teams are becoming smaller, and workflows more dynamic. The question that arises is: do traditional agile methods still make sense in this new context? And if so, how must they evolve to accommodate this new reality?

AI as a New Gear in the System

Agile frameworks were designed to coordinate teams composed of specialists with well-defined roles.

Sprints, standups, and retrospectives were created to synchronize these skills and minimize bottlenecks. But the rise of AI is changing this dynamic.

Today, AI already performs tasks previously assigned to developers, such as code refactoring, generating automated tests, and suggesting performance improvements. This reduces the need for continuous integration among multiple specialists and can lead to leaner teams. However, despite these conveniences, AI is still a complement, not a complete replacement, for human skills.

The full-stack developer, once somewhat sidelined, is becoming relevant again in projects that rely more on AI for coding. But this doesn’t mean that AI eliminates the need for a collaborative team. It helps automate repetitive tasks, but strategic and creative decisions still heavily depend on human interaction.

From Team to Flow

With fewer people directly involved in operational tasks, management needs to shift focus.

If the main goal used to be ensuring team efficiency, now the priority is optimizing the workflow, now impacted by AI. The transition is clear:

• Less people management, more flow management.
• Fewer meetings, more asynchronous execution.
• Less manual planning, more data-driven decisions.

This means that tools and frameworks need to evolve accordingly. Some practices are already changing:

• AI integrated as a partner in sprints, automating everything from code generation to requirements analysis.
• Backlogs prioritized by predictive models, enabling management based on pattern analysis and real needs, though always with human oversight to maintain context.
• Less frequent standups, with real-time updates provided by smart tools, though human monitoring remains important, especially in remote or distributed teams.
• Retrospectives incorporating prompt curation, best practices for AI use, and adjustments for human-machine interaction.

The Future: New Frameworks, New Rules

Traditional agile frameworks were built for a world without AI. While their principles remain relevant, their application will need to be adjusted for an environment where automation is not a detail, but a central element.

The next step will be creating new agile management frameworks, or adapting existing ones, that embrace this shift and allow small, highly productive teams integrated with AI to deliver more value with less bureaucracy, while maintaining the human oversight necessary to ensure quality and alignment with project goals.

Conclusion

Agile methods still play a fundamental role in software development, but their application needs to evolve.

Human-to-human collaboration is giving way to human-AI collaboration, which demands new ways of organizing work. The transition from teams to flows is inevitable. Companies that manage to adapt their methodologies to this new reality will gain efficiency, reduce waste, and accelerate their deliveries.

Whoever understands this change first will take the lead. Now the question is: Is your team ready for this evolution?