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In the current artificial intelligence ecosystem, conversational agents and recommendation systems have been designed under a very convenient premise: the user knows exactly what they want. This assumption, however, clashes with the everyday reality of anyone facing an extensive catalog or a complex task. An online shopper, a data analyst, or a hiring manager rarely have all their preferences defined in advance; they need to explore, compare, and above all, learn. The most recent research in information economics formalizes this phenomenon through the Search-Experience-Credence model, now applied to agent-user interaction. Instead of assuming an expert, the agent must become a facilitator that helps the user build their own preferences, providing them with examples, explanations, and relevant context.
This paradigm shift is especially relevant for companies developing custom applications with artificial intelligence components. An agent that does not limit itself to asking “What do you prefer?”, but rather guides the user through a discovery process, can make the difference between a frustrating experience and a truly useful one. For example, in a technical product recommendation system, the user is likely unaware of the differences between certain processors or storage technologies; the agent must teach them these concepts through comparisons or summaries before asking about their preference. This ability to “teach while conversing” is precisely what distinguishes next-generation agents, and its implementation requires custom software that integrates domain logic, language models, and feedback mechanisms.
From a technical perspective, the challenge lies not so much in searching for items, but in how the interaction expands the user's knowledge of what they truly desire. Experiments with frontier models show that even after five dialogue turns, accuracy barely reaches 56%, indicating a huge room for improvement. For organizations seeking to effectively implement AI for businesses, this implies rethinking agent design: it is not about responding faster, but about accompanying the user in a guided learning process. At Q2BSTUDIO, we work with companies that need to integrate these principles into their platforms, combining AWS and Azure cloud services to scale natural language processing, business intelligence services to analyze user behavior, and Power BI to visualize how preferences evolve over time.
Building an agent that truly helps define preferences requires a multidisciplinary approach where custom application development merges with conversational artificial intelligence. A pre-trained model is not enough; a logic layer is needed to manage the flow of questions, offer examples when the user hesitates, and adapt recommendations based on acquired knowledge. Furthermore, cybersecurity plays a crucial role, as these systems handle sensitive data about user preferences and behavior. Implementing protection protocols and regulatory compliance is part of any robust solution. At Q2BSTUDIO we offer artificial intelligence solutions for businesses that address these aspects, helping our clients create agents that not only recommend but also educate and empower the user.
Finally, continuous monitoring of these interactions allows refining models and detecting patterns that would otherwise go unnoticed. With business intelligence services and tools like Power BI, companies can measure the real impact of their agents: how many times the user needed help defining a preference, what type of examples were most effective, and where drop-offs occur. This data feeds back into custom software design, creating a cycle of continuous improvement. If your organization is ready to go beyond the expert user and build agents that accompany the decision-making process, we invite you to learn how we develop custom applications that integrate these capabilities naturally and efficiently.
