Development-AI: how artificial intelligence is changing software development

Introduction

The concept of AI development is revolutionizing the way we think, design, and create software. It is no longer just about generating code snippets from a prompt, but about building an entire ecosystem in which artificial intelligence becomes a digital colleague capable of collaborating in all phases of the development cycle. From planning to writing, from testing to documentation, and up to code governance, AI is becoming an integral part of the DevOps flow.

In this article, we will explore what AI development means, what the most widely used tools are, how the new principles of Agentic AI fit in, and why software governance will be one of the key challenges in the coming years.

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What is AI Development

The AI-development is the set of practices, tools, and methodologies that integrate generative artificial intelligence models within the software lifecycle. Unlike traditional automation, here we are talking about systems capable of:

• Generate code (AI code generation) based on natural prompts.
• Refactor and optimize existing code (AI refactoring).
• Automatically document repositories and projects.
• Participate in software governance, helping to ensure quality, security, and transparency (AI software governance).

This is a groundbreaking shift, similar to the transition from Waterfall to Agile: it not only changes the way code is written but the entire organization of work.

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The main tools

Among the most widely used tools in AI development today are GitHub Copilot, Cursor AI, and Replit Ghostwriter:

• GitHub Copilot: the pioneer, based on Codex and now on GPT-4. Great for contextual suggestions while writing code.
• Cursor AI: an IDE built around AI, with the ability to understand entire repositories and work on complex refactors.
• Replit Ghostwriter: integrated into an online development environment, designed for those who want to prototype quickly.

These tools do not just generate code: increasingly, they integrate with CI/CD pipelines, documentation systems, and testing tools.

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Agentic AI in Software Development

The next step in sviluppo-AI is the arrival of Agentic AI: models that do not just respond to prompts but act as true agents autonomously with specific roles. Imagine an AI team composed of:

• Planner → defines objectives and roadmap.
• Coder → writes and optimizes code.
• Tester → executes automated scenarios and finds bugs.
• Auditor → checks compliance with security and compliance standards.

These agents collaborate with each other and with human developers, creating a hybrid workflow that combines speed, accuracy, and oversight.

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Practical Cases of AI Development

The AI-development is not theory: there are already concrete examples of daily application.

Automatic Refactoring

Through tools like Cursor, it is possible to pass a “dirty” function to an AI with unclear variable names and receive in response a refactored, readable, and commented code.

Deepen the comparison between GitHub Copilot, Cursor AI, and Replit Ghostwriter

Automatic Documentation

Thanks to scripts connected to Git hooks, it is possible to automatically generate updated README.md and CHANGELOG.md files with each commit, reducing the risk of outdated documentation.

RAG by code

With Retrieval Augmented Generation techniques, it is possible to create “queryable” repositories from AI, reducing hallucinations and retrieving existing snippets based on context.

IT Incident Classification

In the field of security and compliance, AI models can automatically categorize incidents recorded in systems like SharePoint or ServiceNow, helping to comply with directives such as DORA and NIS2.

Explainability and Governance

Companies are experiencing AI that not only writes code but also explains how it works, generates ADR (Architecture Decision Record), and creates audit trails to ensure transparency and accountability.

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Benefits and Limitations

The benefits of sviluppo-AI are evident: increased productivity, reduced technical debt, updated documentation, faster release times. However, there are also limitations:

• Hallucinations: AI can produce syntactically correct code but logically incorrect.
• Context dependence: without well-structured repositories, performance drops drastically.
• Governance and accountability: who is responsible for an error introduced by AI?

For this reason, AI development must always be accompanied by strong human oversight and validation processes.

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Consulting and Services

Integrating AI into software development requires cross-functional skills: knowledge of tools, the ability to adapt them to business processes, and attention to privacy and compliance. The services I offer include:

• Repository audit to verify if it is AI-ready.
• Implementation of AI-assisted pipelines in CI/CD processes.
• Team training on prompt engineering and best practices.
• Consulting on governance of AI development, focusing on risks and opportunities.

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Conclusion

The AI development is not a passing trend, but a structural transformation in the way software is created. Today is the right time to experiment, integrate, and govern these tools to gain a real competitive advantage.
👉 Do you want to prepare your team for AI development? Contact me for a personalized consultation.