We are at the threshold of a disruption without precedent. What we now have at hand is a new kind of cognitive assistance that not only automates, but also understands, reasons, and helps us decide.
What makes this stage different is that artificial intelligence is neither a luxury nor a lab-bound prototype: it is accessible, scalable, and applicable to almost any discipline. This opens two complementary dimensions:
- AI as a Component: native to software systems and architectures. Just as no one imagines an app without a database, soon it will be unthinkable to develop a digital product without an embedded artificial intelligence component.
- AI as an Operational Copilot: today no one envisions business processes without email or ERP, and in the near future it will be difficult to imagine processes without some degree of AI intervention, whether in their design or in the way they work.
These two dimensions do not compete; they complement each other. One transforms the way we design and build systems; the other, the way we work every day.
From Software to Connected Knowledge
In the past, organizations—not only in IT but also in banking, telecommunications, insurance, and government organizations—would develop software almost in bulk, as though on a factory line.
Artificial intelligence offers a whole new scenario. It is not only about developing apps, but about producing living knowledge that adapts and evolves in dynamic contexts. Such knowledge materializes in multi-agent connective architectures, which is to say, intelligent agents’ networks that collaborate with each other and with people. In these networks, one agent understands the client, another one processes documents, while others integrate systems or recommend business actions. The real power lies in the connection, because together they solve problems more effectively than any traditional monolithic app.
The transition is clear: we move from only writing rules to modeling agents that reason, and from creating closed apps to building open and adaptive architectures. Above all, we evolve from processing operations to generating real-time emergent value, while harnessing the latent potential of LLMs, contextualized with data and proper governance.
To make this collaboration happen, new integration protocols are emerging, such as Agent-to-Agent (A2A) and Model Context Protocol (MCP). These are the first attempts at a “common language” between agents, allowing us to move from isolated pilots to true agentic ecosystems capable of solving complex workflows.
In this way, software factories instead become factories of connected knowledge, where the output is not only code, but cognitive systems that evolve and learn with every interaction.
The Road ahead
In this new paradigm, design is no longer about drawing screen layouts, it is about orchestrating behaviors, building relationships between people and intelligent systems.
This opens the door to what we call “agentic experiences”, where interaction is no longer only between a person and a machine, but among a person, an agent, and a system. There are no longer two players; now there are three, and agents play an increasingly powerful role.
From this transition, new patterns that will shape the digital experience of the coming years emerge:
- Human–machine collaboration: agents that know when to intervene and when to step back for human decision-making.
- Contextual adaptation: experiences that adjust to users and environments in real time.
- Ambient intelligence: services that operate almost invisibly but generate tangible impact.
- Decision support: artificial intelligence that helps people make better choices rather than replace them.
These patterns do not emerge on their own. They require agents to connect, share data, and coordinate tasks. This is where orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, Autogen, among others) play a central role, because they make it possible to chain models, tools, and data sources, enabling comprehensive and consistent experiences.
Where to Begin?
All of this sounds powerful, and we are already experiencing it while there is fierce competition among various models: GPT-5, Gemini, Claude, Llama, Mistral, each pushing the limits in different dimensions (multimodality, context size, efficiency).
There is no single standard or dominant model today, because it is still unclear whether “raw” intelligence will prevail, or whether differentiation will emerge through attributes such as speed, integration, or cost. Meanwhile, many organizations are still watching from afar. That is understandable: starting from zero and blindly carries risks, but so does inaction.
The key is not to choose “the winning model,” but to design a flexible strategy that can adapt as the scenario evolves. The second step is to advance incrementally, earning confidence and managing risks with each iteration.
A possible path is:
- Start with a controlled first contact: test operational copilots in simple tasks such as answering internal queries, summarizing documents, or automating reports.
- Identify real frictions: choose a concrete pain point—an operational bottleneck or repetitive process—and deploy an agent to address it.
- Iterate and learn: measure results, refine prompting, governance, and processes. Gain confidence with tangible cases.
- Scale to multi-agent architectures: connect multiple agents to solve more complex problems with shared decision flows.
- Build a reusable agent catalog: turn internal knowledge into a product offering, thus creating a base of agents that can serve multiple teams and projects.
In parallel, data governance becomes critical. It is not only about safeguarding information but ensuring that models can access the right and relevant information at the right moment. Concepts such as Context-Aware Retrieval (CAR) and Generative Augmented Retrieval (GAR) help reduce hallucinations and increase reliability, guaranteeing that agents work with meaningful data rather than noise.
This path is neither linear nor identical for each organization, but it enables something crucial: moving forward without losing control and adding value with every stage.
At Flux IT, we work with many organizations that have spent years trying to connect their internal knowledge. Today, we understand that this new AI paradigm makes that knowledge actionable in an autonomous, contextual, and operational way. It is no longer just about knowing where information resides; it is about having systems that understand it, combine it, and act on it in real time. In other words, knowledge moves from being a support function to becoming the driving force of operations.