In the modern software development flow, we often underestimate the value of the discovery phase. It is at this moment, whether initial, or iterative, that insights are born, vision is ignited, and the first point of contact between team, product, and user is created. It is also the moment when prototypes take on a key role.
The Evolution of Prototypes
Prototypes are often generated based on tools such as figma, wireframes or Draft applications in order to interact with the customer, prototypes in which an attempt is made to capture the essence of an idea. But that code, while raw, has a deeper purpose: to stimulate conversations, to validate insights, to generate alignment between business, design and technology. It should not be just a technical exercise, but the creation of a bond, a collaboration with the customer.
Today, thanks to AI agent-based tools, this kind of prototyping takes a new form that changes the interaction with stakholders. Where before the process was fragmented and asynchronous (show, gather feedback, edit, schedule a new meeting) it now becomes fluid and instantaneous. It is no longer about writing code quickly, or creating wireframes and/or figma but about generating interactive experiences in minutes, even while talking to the stakholder by putting continuous dialogue at the center rather than time-diluted review cycles.
AI Agents and the New Discovery
With the rise of AI agents, we are seeing a radical transformation of the discovery phase. In my experience, I have experimented with different tools, each with its own peculiarities and specific use cases.
Lovable: From Idea to Prototype in Natural Language
Lovable stands out for its accessibility. Imagine you need to quickly prototype an app for managing shared expenses in a group of friends. Instead of drawing wireframes, you can simply write, “Create an app where users can add expenses, share them among group members, and see who owes what to whom.”
In just a few minutes, Lovable generates a working interface complete with an expense entry form, participant list, and sales dashboard. The added value? You can immediately share the link with the customer and gather concrete feedback.
I like it, but I wish the owes were more visible
instantly becomes a new iteration.
bolt.new: Transparency for Developers
bolt.new takes a different approach, constantly showing the generated code. When I had to prototype a reservation system for a coworking facility, I appreciated how each change was translated into specific commands. By typing “Add a calendar with available time slots,” I could immediately see the generated React component and figure out how to integrate it later into the main project.
The choice between React, Vue, Svelte or even React Native for mobile allows you to prototype directly into the target technology, reducing the gap between prototype and final.
From Prototype to Insight
These tools are redefining Discovery because they turn every conversation into an opportunity for prototyping. No longer “Imagine if…” but “See what happens if….” They lower technical barriers, speed up feedback loops, and make it possible to prototype with the customer, not just for the customer.
The real revolution is not in the speed of generation, but in the ability to iterate with the customer, to see his or her reactions in real time on something tangible and working, to turn every “though” and every “if instead” into a new version of the prototype within minutes.
A Prototype for Everyone
Thanks to AI agents, teams today can quickly build exploratory versions of the product to show to stakeholders, test micro-functionality or simulate real-world user experiences in a matter of hours. These are no longer just mockups, but interactive prototypes with high emotional fidelity.
The Beating Heart of Collaboration
In this evolution I see the essence of the **Agile Manifesto **again. From AI-generated prototypes emerge conversations, fueling ongoing dialogue between teams and stakeholders, turning any feedback into an opportunity for immediate iteration.
Even if incomplete, even if crude, these digital artifacts generate tangible and immediate value: they allow people to touch an idea, to experience it by giving value to the dominance of “working software” over documentation.
It is in “collaboration with the customer” that AI agents show their real revolution. It is no longer about presenting a predefined solution and negotiating its details, but co-creating in real time, building the experience together while discussing it. The customer becomes an active part of the creative process, no longer just the end recipient.
And when change becomes inevitable, as is often the case in complex projects, the ability to “respond to change” translates into how quickly an AI prototype can adapt, evolve, reinvent itself. What once required weeks of reprogramming can now be changed in a matter of hours, allowing teams to embrace uncertainty as a source of opportunity rather than an obstacle. Having a prototype aligned before moving to the real implementation phase within an iteration helps anticipate change.
Toward Continuous Discovery
AI agents are democratizing prototyping. Discovery is no longer an early and isolated phase, but a continuous and distributed process. It is no longer the exclusive task of the UX team, but a shared practice among designers, developers, product managers, and stakeholders.
Conclusion
Prototyping with AI agents means building to understand, creating to validate, exploring to align.
In a world where change is the norm and solutions are always temporary, the ability to prototype with speed, intelligence, and authenticity is what separates learning teams from those just trying to keep up.
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