arXiv:2605.07717v2 Announce Type: replace-cross Abstract: Despite the widespread adoption of agile methods, achieving true agility at scale remains elusive. Large-scale agile frameworks remain largely human-centric and manual, relying on coordination meetings, artifact synchronization, and role-based handoffs that inhibit real-time adaptation. Meanwhile, rapid advances in AI, particularly large language models, have begun transforming software engineering, yet their potential for organizational-level agility remains underexplored. We present the AI-Native Large-Scale Agile Software Development
The proliferation of advanced AI, particularly large language models, has reached a point where their application to complex organizational processes like large-scale agile development is becoming feasible.
This development suggests a fundamental re-architecture of software development methodologies, potentially leading to significant gains in efficiency, adaptability, and speed for large organizations.
Traditional human-centric agile frameworks, which often struggle with scaling, will likely be augmented or replaced by AI-native approaches that can manage complexity and adaptation in real-time.
- · AI software development tool vendors
- · Large enterprises adopting AI-native agile
- · Software engineers leveraging AI assistants
- · Cloud providers
- · Traditional agile consultancies
- · Companies slow to adopt AI-native methods
- · Manual coordination roles in large development teams
Software development cycles will shorten significantly, and output quality will improve.
The competitive landscape for software-intensive industries will accelerate, creating winners and losers based on AI adoption rates.
AI-driven development could lead to a self-improving software ecosystem, where AI creates better AI development tools, further accelerating innovation.
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Read at arXiv cs.AI