Large language models are evolving rapidly, giving engineering teams new ways to work more efficiently. Developers and software architects are already using these tools to improve their daily workflows.
For CTOs and CIOs, the next step is to extend these benefits across the organisation.
This means moving from individual AI assistants to a shared technical framework. The goal is to make AI a standard part of software development, accessible to every engineering team.
The limits of fragmented AI adoption
When only a few specialists use AI, the benefits rarely extend across the organisation.
This fragmented approach can lead to inconsistent code quality. It can also create security risks when sensitive data is shared outside controlled environments.
To deliver business value, companies need a structured approach to AI adoption.
An AI-native operating model provides a framework for governance. It connects automated workflows to existing development tools, project management systems and codebases.
This approach defines how, when and by whom generative AI models are used throughout the software development lifecycle.
How does agentic infrastructure improve software engineering?
AI integration is moving beyond standalone assistants.
Agentic infrastructure allows AI to interact directly with a company's technical environment. Instead of responding to isolated prompts, the language model can access relevant business context, version history and application dependencies.
We recently implemented this approach for a client that develops digital workflow solutions.
Our engineering team used integration standards such as the Model Context Protocol (MCP) to connect Claude to the client's MongoDB databases and Jira environment.
This gave the AI access to relevant development information in real time.
Engineers no longer needed to provide the same context manually for every task. The AI could generate recommendations based on the company's existing software architecture and coding standards.
The agentic infrastructure also allowed AI to become part of the continuous integration and continuous delivery (CI/CD) pipeline, supporting code reviews before production deployment.
Building an AI-native model around business needs
Introducing a new technical framework requires a structured implementation process.
Through our work as a technology and AI partner for software companies, startups and SMEs, we have developed an approach centred on business needs.
The first step is to give the AI model sufficient context.
This involves configuring specific capabilities that help the model understand the existing codebase, technical documentation and development history.
We then introduce the new workflows through short development cycles.
A pilot group tests their reliability within a limited scope before the approach is extended to other engineering teams.
Prompts and instructions are documented and version-controlled, just like source code.
This creates a more consistent and scalable infrastructure that teams can maintain as their needs evolve.
How can you measure the results of AI adoption?
An AI-native operating model can improve engineering productivity by reducing time spent on repetitive technical tasks.
Across our client projects, we have seen significant reductions in the workload associated with code reviews and regression testing.
A recent AI-native operating model project for a B2B software company illustrates this approach.
By integrating Claude through MCP, our engineers helped reduce the time needed to resolve complex architecture tickets by 70%.
The average resolution time fell from two weeks to four days.
An engineer can now close three to four major tickets per day. Previously, the same volume of work required an entire 20-day sprint.
These improvements give technology, data and product teams more time to focus on strategic priorities.
These include modernising software architecture, reducing technical debt and developing new features.
Working with a technology and AI partner to support adoption
Scaling AI requires ongoing support and an understanding of how the technology is evolving.
Novity established a dedicated AI practice in January 2026. Since then, our teams have worked on more than 15 strategic projects.
Our engineers and AI architects are based across Madagascar and Mauritius.
This multi-location model gives us the flexibility to support complex projects in FinTech, retail, healthcare and B2B services.
In October 2026, Novity became a certified member of Anthropic's Claude Partner Network.
Our internal academy, launched in May 2026, also supports continuous technical development.
More than 15 of our engineers have achieved the Claude Certified Architect – Foundations (CCA-F) certification.
They work alongside development, AI and product teams to help organisations adopt these technologies in a structured and secure way.
Four pillars of agentic infrastructure
→ Context : Give LLMs such as Claude, GPT and Gemini access to relevant codebases and technical documentation.
→ Connectivity : Connect AI models to databases and development tools through standards such as MCP.
→ Governance: Establish regular reviews to improve AI instructions, share knowledge and protect sensitive information.
→ Validation: Test the approach with a pilot group before rolling it out across the organisation.
