It is no coincidence that no language on Earth has ever produced the expression “As easy as developing enterprise software.”

From in-flight digital control systems to energy grid management platforms, to connected health devices, and B2C digital services with millions of users – complex software products and platforms are at the heart of all modern products and services. And yet, the way we build them hasn’t changed much in decades. Siloed teams. Long cycles. Endless handoffs. Testing bottlenecks. Rework loops.

A lot of this is slow and laborious, yet understaffed software teams must plough through it, often at the expense of higher value but less urgent software they could be writing. But change is finally here – and it’s big.

As AI becomes more accessible, and different AI tools can work together, it is helping software engineers work more efficiently across the development lifecycle, augmenting their capabilities, and cutting out the dull, laborious tasks: from writing the same old code, to slogging through endless revisions. People and processes, products and services will move to new levels of intelligence and effectiveness. Enter Agentic AI.

Enter Agentic AI: the beginning of the end for the traditional SDLC

Generative AI has already shown remarkable promise in augmenting, accelerating and improving individual steps within the software development lifecycle (SDLC) – generating code, writing tests, converting legacy syntax, or spotting bugs. In fact, 85% of software professionals expect to use Gen AI in 2026 to augment tasks like coding and user story generation, up from 46% in 2024, according to research by the Capgemini Research Institute.

So, it’s already being used by software engineers at discrete points to augment the SDLC – but how can it help across the whole lifecycle?

That’s where Agentic AI makes a big impact. Developed by highly skilled engineers, AI agents go beyond the capabilities of individual Generative AI models, enabling multi-step reasoning across different tasks, extended task memory, and the ability to work with external tools. They can even execute actions independently where developers deem it safe for them to do so, bringing greater autonomy to entire workflows. 

With agentic systems, multiple AI agents operate together, each applying their unique expertise and capabilities, communicating with each other, and collaborating as part of a real-world software team. These agents don’t just complete isolated tasks – they work together, constantly aligning, adjusting, and improving as they move through the software development process with human oversight.

Imagine a developer overseeing a set of agents: a test agent validating output from a build agent, then notifying a debugging agent, which corrects the issue and triggers a retest – all in seconds. A task that once frustrated development teams with days of back-and-forth now happens almost instantly.

This isn’t theoretical—it’s happening now, with engineers guiding the application of Agentic AI in real-world environments.

With the support of skilled engineers, this technology can be deployed to augment wide-ranging software challenges spanning the SDLC. Creating new software products or adding innovative features and capabilities can be hyper-accelerated, meaning software products get to market much faster.

Upgrading software architectures or refactoring codebases for improved performance can happen in a fraction of the time, and with fewer revisions. We stand at the precipice of a new world of software engineering – in which identifying and taking new product offerings to market, hyper-customization of software products and even developing new features in near real-time will become the norm.

Not only does this human-plus-AI approach significantly streamline the SDLC and improve efficiency by orders of magnitude, but it also delivers more secure, accurate outcomes. They allow for an effective feedback loop, where AI no longer just delivers isolated tasks, but creates agents that collaborate as a team.

The big difference is that dialogue between team members happens automatically and instantaneously. A back-and-forth between a human coder and a tester to iron out a bug can take a day or two to resolve, because each interaction is a distraction to other work they are trying to complete. But when AI agents perform those roles, it happens in seconds, stripping out bottlenecks and slashing development times, whilst allowing developers much needed time to focus on more complex code, creative problem solving and other interesting high-value tasks.

Our solution: A suite of agents that works across the software development lifecycle  

Since 2023, our best software product and platform engineers have been researching ways to harness collaborative AIs to better support software teams, even before Agentic AI became a buzzword. The result is our newly launched ‘RAISE for Software Product X’.

Behind the technical name is a transformative solution – a suite of four macro agent families developed by skilled engineers, each containing various AI agents tailored to engineering tasks, across the SDLC, which talk to each other, mirroring the iterative process of development teams. These agents, together with an orchestration framework, internal control logic and a unique ‘metamodel’ concept, comprise the powerful toolkit we’re using to solve the delays and bottlenecks that plague Software Product Engineering.

The first of these macro agents sits at the front-end of the SDLC. The Product Optimizer Agent assesses requirements and opportunities for product value. It analyses documentation, user input, customer support data, market insights, product requirements and the codebase of a company’s existing software portfolio. This allows it to provide a view of the current landscape and suggest improvements to software teams, like how to resolve bugs, reduce tech debt, deliver innovative new features or improve security.

Next is the Product Creator Agent. This takes in marketing or user requirements, and breaks those down into technical requirements, defining epics and user stories, generating and refining code builds, testing and deploying products into production. Critically, whilst AI delivers the execution, human engineers still oversee, validate, and guide key decision points, ensuring trust and control throughout.

A third micro agent family focuses on radically improving the development of microservices-based applications through domain-driven design. The Product Domain Modeler Agent helps teams quickly map out how a software system should be built from front-end and back-end microservices, to data connections, so new or modernized software products can be built faster and with fewer iterations.

Finally, the Product Migrator Agent family brings together a host of agents that reverse engineer legacy codebases, improve and enhance code documentation, build a foundational understanding of existing code and transform legacy code into new languages or refactored code.

Introducing: RAISE for Software Product X

At this point, you are perhaps intrigued, but likely cautious (or even cynical) about this radical approach to transforming and augmenting your SDLC with Agentic AI. Surely, separate AI agents cannot simply jump into your software development workflows and communicate with each other without some shared knowledge of the system they are working with – any more than humans can?

But this is where Capgemini’s RAISE for Software Product X is unique. The four micro agent families work together to create a shared and constantly updated ‘metamodel’ that operates like a master blueprint or digital twin of the essence of a company’s software products and platforms. The metamodel represents every aspect of the software product: the problems it solves, the requirements, the business logic, APIs called, connections to other systems, class libraries used, and so on.

With human engineer oversight, it allows all agents to continuously align goals, grounded in a deep understanding of all aspects of the product, while ensuring everything works within the current architecture, even as it evolves. So, for example, the Product Migrator Agent does not directly translate old software – and all its problems – into a new language; it rewrites it to optimize its capabilities holistically and proposes new code for human teams to work with.

RAISE for Software Product X has already helped large global engineering companies cut software product development and modernization time by up to 50% – with initial trials quickly progressing to larger projects.

In one instance, a major energy and utility client used it to modernize an energy grid management system. Their system had evolved over several years and was fragile, while also containing highly sensitive code. Capgemini Software Product Engineering leverages RAISE for Software Product X AI agents into a solution to iteratively redesign and refactor the application to a modern, microservices-based architecture, on which future enhancements and features could be built more quickly.

The customer was so impressed and excited with the results that they’re now working with Capgemini to roll out agentic engineering capabilities across their entire SDLC.

RAISE for Software Product X isn’t just another tool. It’s a new way to enhance how teams approach software development, combining human insight with AI efficiency to allow overstretched software teams to get twice as much done.

So, if you want to give your software teams the tools to slash software product development times, improve code quality, innovate faster, and modernize legacy software products, now is the time to get in touch.

Capgemini is ready to be your partner and guide in building your Agentic Software Engineering future – from pilot to enterprise-wide transformation and user training. Discover more about RAISE for Software Product X and contact us for a demonstration.