AI Software Development: What It Is and How the Software Lifecycle Is Changing
Software development is undergoing a historic transformation. Unlike past technological shifts -mostly driven by faster tools or new frameworks – we are now facing a genuine paradigm change: generative AI, and in particular specialised AI agents, are influencing multiple processes and phases of the Software Development Lifecycle (SDLC). Together, they define what we now call AI software development.
GitHub reports that 92 % of US developers already use generative-AI tools such as Copilot inside or outside work, and more than 70 % say they deliver higher-quality code faster: this is clearly not a niche trend but a mass migration to a new paradigm. This rapid adoption signals the dawn of AI agent software development, where specialised agents collaborate with humans across the entire SDLC.
What does this evolution actually mean, and what are its implications for the software industry? Let’s break it down.
AI Software Development Lifecycle: From Requirements to Maintenance
Large Language Models’ (LLMs) capacity to generate code is well documented, adopted by 82 % of developers, and improving with every release—better syntax patterns, deeper contextual inference, broader framework coverage. Yet writing code is not the same as designing software.
Software engineering is a broad, multi-step discipline: defining and validating requirements, modelling scalable architectures, designing module interactions, creating and running tests, ensuring quality and managing long-term evolution. Not every task involves writing code, but each is central to a robust, secure and future-proof SDLC.
This is where generative AI – especially AI agents based on LLMs or Small Language Models (SLMs) -introduces a genuine discontinuity, to the point that people now speak of the AI software development lifecycle. Rather than merely accelerating isolated rule-based tasks, AI lets us reimagine many SDLC stages – requirements, design, coding, testing, maintenance—as assisted, conversational workflows.
AI software development is therefore a new way of working and interacting with the tools that build and manage software systems. It is not a hand-off of control but an amplification of the team’s collective intelligence, with a co-pilot that supports every SDLC phase.
Agentic AI Software Development: From Co-Design of Use Cases to Functional Requirements
In the AI software development lifecycle, gathering and defining functional and non-functional requirements remains essential. But rather than relying almost exclusively on analysts’ experience, teams can now adopt an evolved co-design approach, powered by AI agents.
AI can actively parse initial requests, suggest use cases relevant to the domain, hypothesis interaction scenarios and even flag potential design gaps.
When properly trained, these assistants do more than generate documentation: they explore design variants, produce first-draft requirements aligned with the corporate tech stack and recommend proven architectural patterns. The result is a smoother, more continuous definition process where AI helps teams create a solid project foundation.
Support for Code Writing and Validation
Coding is still the core activity in software development, traditionally reliant on developers’ skill and experience. While programmers have always had some automated help – debuggers, linters – AI agents provide far more proactive support: they can generate blocks of code and suggest context-aware solutions.
McKinsey Digital’s latest empirical research shows that generative-AI tools can:
- halve the time needed to document code for maintainability,
- cut almost 50 % off the time required to write new code,
- complete refactoring in roughly one-third of the usual time.
With the right upskilling and enterprise enablement, these speed gains translate into unprecedented productivity in software engineering. Beyond acceleration, advanced tools also safeguard coherence and quality by highlighting potential errors and proposing intelligent refactoring.
Automation and Optimisation of Testing
Testing – long a critical SDLC phase – rests on analysing requirements, defining test cases and executing them. Historically, this has been under-automated and therefore slow and error-prone. By embracing AI for development software initiatives, organisations can turn testing from a bottleneck into a competitive differentiator.
With AI agents, testing changes radically. AI can read requirement documents – regardless of structure, syntax or language – and automatically generate coherent test cases, including edge cases and complex interaction scenarios.
Given that a single application may require hundreds or thousands of tests, AI agents’ ability to design, implement and even run those tests – writing the necessary code – dramatically shrinks timelines. A regression cycle that once took weeks can now be compressed into days, raising overall verification and validation effectiveness.
Proactive Maintenance and Upgrades
Embedding AI agents into maintenance and workflows turns a traditionally reactive process into a proactive and predictive one. Alongside accelerating classic tasks such as bug fixing and refactoring, intelligent agents can anticipate issues before they surface.
By analysing patterns in system logs, performance metrics and user feedback, well-trained AI agents detect components at risk, suggest targeted optimisations and even orchestrate critical patch deployment. Monitoring real-world behaviour, they can flag under-used or obsolete features, guiding simplification or re-engineering decisions that improve efficiency and usability.
AI Agents are Tools, Not Substitutes
Despite their advanced capabilities, AI agents remain cognitive amplifiers, not autonomous replacements. They must be guided and cannot be let loose unsupervised.
The reason lies partly in LLM idiosyncrasies (hallucinations)and partly in their limited grasp of a project’s strategic and organisational context. An AI agent may spot performance bottlenecks and propose fixes, but only an experienced developer can judge their deeper implications: roadmap evolution, alignment with unspoken business requirements or team-backlog balance.
This is where human-AI augmentation shines. Intelligent agents excel at analysing vast operational datasets, spotting patterns across long timescales and correlating signals from disparate systems. By off-loading that heavy lifting, teams can focus on what really matters: architectural decisions and innovation with full-system vision.