It is no coincidence that no language on Earth has ever produced the expression “As easy as developing enterprise software.” Because it isn’t.
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.
But change is finally here – and it’s exponential. As AI moves from the niche to the mainstream, all engineering activities and products will become augmented. People and processes, products and services will move to new levels of intelligence and effectiveness.
Enter Agentic AI: the beginning of the end for 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 are expected to use Gen AI in 2026 for 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 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. On top of generative AI’s tremendous capabilities, Agentic AI adds extended memory, multi-step reasoning and the ability to use external tools and execute actions, enabling autonomy across multiple systems and workflows.
With agentic systems, multiple intelligent agents operate together, each applying their unique expertise and capabilities, communicating with each other, and collaborating like real-world software teams. 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 test agent validating output from a build agent, then notifying a debugging agent, which corrects the issue and triggers a retest – all in seconds, with no human intervention. What once took days of back-and-forth now happens instantly.
This isn’t the future. It’s already here.
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 higher quality. 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 team-style Agentic AI drastically accelerate the SDLC and improve efficiency by orders of magnitude, but it also delivers more secure, accurate outcomes, while emulating the ways that the best human engineers work – research, write, reflect and validate. The agentic framework allows for an effective feedback loop, where AI is no longer just delivering isolated tasks, but creating agents that collaborate as though they’re part of a ‘real-world’ 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 can take a day or two to resolve. But when AI agents perform those roles, it happens in seconds, stripping out bottlenecks and slashing development times.
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, even before Agentic AI became a buzzword. The result is our newly launched ‘Software Product X Agentic Framework by Capgemini’.
Behind the technical name is a transformative solution – a suite of four micro agent families, each with dozens of AI agents with different skillsets across the SDLC, which talk to each other, mirroring the iterative process of human 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 bring Software Product Engineering to the next frontier.
The first of these micro agents sits at the front-end of the SDLC. The Software Product Optimizer plays the roles of Product Owner, Analyst, Consultant and additional roles, assessing 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, like how to resolve bugs, reduce tech debt, deliver innovative new features or improve security.
Next is the Software Product Creator. This acts as the core engineering team, taking in marketing or user requirements, breaking those down into technical requirements, defining epics and user stories, generating and refining code builds, testing and deploying products into production with minimal human intervention – yet still keeping the ‘human in the loop’ for vital steps and checks.
A third micro agent family focuses on radically improving the development of microservices-based applications through domain-driven design. The Software Product Domain Modeler expedites the definition of software architecture, front-end microservices, back-end microservices, data access and necessary infrastructure, speeding the deployment of new or modernized software products.
Finally, the Software Product Migrator 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.
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 Software Product X Agentic Framework is unique. The four micro agent families work together to create a shared and constantly updated ‘metamodel’ – 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.
This 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.
Reducing software development times, modernizing software, saving money
The Software Product X Agentic Framework 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 has 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 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.
Product X isn’t just another tool. It’s a new way of thinking about software development.
So, if you want to slash software product development times, improve code quality, innovate faster, and modernize legacy software products with a fraction of the time, cost, hassle and risk, 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. Discover more about Software Product X Agentic Framework and contact us for a demonstration.
