Imagine a digital version of yourself that moves faster than your fingers could: an agent with AI who knows your preferences, anticipate your needs and act in your name. This is not just an assistant who responds to the indications; Make decisions. Scan options, compare prices, filter the noise and complete purchases in the digital world, all while making your day in the real world. This is the future for which many AI companies are being built: ai agenic.
The brands, platforms and intermediaries will implement their own tools and artificial intelligence agents to prioritize products, objective offers and closures agreements, creating a new digital ecosystem of the size of a universe where machines speak with machines and humans move to the outskirts. Recent reports that OpenAI will integrate a Chatgpt payment system offers a look at this future: purchases could soon be completed without problems within the platform without the need for consumers to visit a separate place.
Strategy Director at Tustpilot.
AI agents become autonomous
As IA agents become more capable and free, they will redefine how consumers discover products, make decisions and interact with brands daily.
This raises a critical question: when your AI agent is buying for you, who is responsible for the decision? Who do we hold when something goes wrong? And how do we ensure that human needs, preferences and real world comments still have weight in the digital world?
At this time, the operations of most AI agents are opaque. They do not reveal how a decision was made or if commercial incentives were involved. If your agent never surfaces a certain product, it is possible that it is never known to be an option. If a decision is biased, defective or misleading, there is often no clear path for the appeal. Surveys already show that lack of transparency is eroding trust; A Yougov survey found that 54% of Americans do not trust AI to make odd decisions.
The issue of reliability
Another consideration is hallucination, an instance in which IA systems produce incorrect or totally manufactured information. In the context of customer assistants with AI, these hallucinations may have serious consequences. An agent can give a confidantly incorrect response, recommend a non -existent business or suggest an option that is inappropriate or misleading.
If an AI assistant makes a critical error, such as booking a user at the wrong airport or misrepresenting the key characteristics of a product, user's confidence in the collappee system is likely. Trust once broken is difficult to rebuild. Unfortunately, this risk is very real without continuous monitoring and access to the latest data. As an analyst said, the Adagio still contains: “garbage inside, garbage out.” If an AI system is not maintained properly, it is regularly updated and carefully guided, hallucinations and inaccuracies will inevitably drag.
In higher risk applications, for example, financial services, medical care or trips, additional safeguards are often necessary. These could include human verification steps in the circuit, limitations in autonomous actions or stepped confidence levels depending on the sensitivity of the task. Ultimately, maintaining user trust in AI requires transparency. The system must demonstrate that it is reliable in repeated interactions. A high profile or criticism may delay adoption significantly and damage confidence not only in the tool, but in the brand behind it.
We have seen this before
We have seen this pattern before with algorithmic systems such as search engines or food on social networks that moved away from transparency in search of efficiency. Now, we are repeating that cycle, but bets are higher. Not only are we shaping what people see, we are shaping what they do, what they buy and what they trust.
There is another layer of complexity: AI systems are increasingly generating the same content in which other agents trust decisions. Reviews, summaries, product descriptions: all rewritten, condensed or created by large language models trained in scraped data. How do we distinguish the real human feeling of synthetic imitators? If your agent writes a review in your name, is that your voice really? Should it be heavy just like the one you wrote?
These are not edge cases; They are quickly becoming the new digital reality that bleeds the real world. And they go to the heart of how trust is built and measured online. For years, verified human feedback has helped us understand what is credible. But when the AI begins to intermediate that feedback, intentionally or not, the terrain begins to change.
Trust as infrastructure
In a world where agents speak for us, we have to consider trust as an infrastructure, not only as a characteristic. It is the basis on which it is based on everything else. The challenge is not just about preventing erroneous information or bias, but of aligning AI systems with the disorderly and nuanced reality of human values and experiences.
Agent AI, done well, can make electronic commerce more efficient, more personalized, even more reliable. But that result is not guaranteed. It depends on the integrity of the data, the transparency of the system and the disposition of the developers, platforms and regulators to keep these new intermediaries to a higher standard.
Rigorous test
It is important that companies rigorously try their agents, validate the results and apply techniques such as human feedback loops to reduce hallucinations and improve reliability over time, especially because most consumers will not analyze all the response generated by AI.
In many cases, users will take what the agent says to the letter, particularly when the interaction feels perfect or authorized. That makes it even more critical for companies anticipating possible errors and generating safeguards in the system, ensuring that trust is preserved not only by design, but by default.
Review platforms have a vital role to play to support this broader confidence ecosystem. We have the collective responsibility to ensure that the reviews reflect the real feeling of the client and are clear, current and credible. Data like this have a clear value for AI agents. When systems can extract verified reviews or know which companies have established transparency and response capacity reputations, they are better equipped to offer reliable results to users.
In the end, the question is not only in whom we trust, but how we maintain that trust when decisions are increasingly automated. The answer lies in a reflexive design, relentless transparency and a deep respect for the human experiences that feed the algorithms. Because in a world where the purchase of AI, it is still humans who are responsible.
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