Imagine a digital version of yourself that moves faster than your fingers ever could – an AI-powered agent that knows your preferences, anticipates your needs, and acts on your behalf. This isn’t just an assistant responding to prompts; it makes decisions. It scans options, compares prices, filters noise, and completes purchases in the digital world, all while you go about your day in the real world. This is the future so many AI companies are building toward: agentic AI.

Brands, platforms, and intermediaries will deploy their own AI tools and agents to prioritize products, target offers, and close deals, creating a new universe-sized digital ecosystem where machines talk to machines, and humans hover just outside the loop. Recent reports that OpenAI will integrate a checkout system into ChatGPT offer a glimpse into this future – purchases could soon be completed seamlessly within the platform with no need for consumers to visit a separate site.

AI agents becoming autonomous

As AI agents become more capable and autonomous, 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’s responsible for the decision? Who do we hold accountable when something goes wrong? And how do we ensure that human needs, preferences, and feedback from the real world still carry weight in the digital world?

Right now, the operations of most AI agents are opaque. They don’t disclose how a decision was made or whether commercial incentives were involved. If your agent never surfaces a certain product, you may never even know it was an option. If a decision is biased, flawed, or misleading, there’s often no clear path for recourse. Surveys already show that a lack of transparency is eroding trust; a YouGov survey found 54% of Americans don’t trust AI to make unbiased decisions.

The issue of reliability

Another consideration is hallucination – an instance when AI systems produce incorrect or entirely fabricated information. In the context of AI-powered customer assistants, these hallucinations can have serious consequences. An agent might give a confidently incorrect answer, recommend a non-existent business, or suggest an option that is inappropriate or misleading.

If an AI assistant makes a critical mistake, such as booking a user into the wrong airport or misrepresenting key features of a product, that user’s trust in the system is likely to collapse. Trust once broken is difficult to rebuild. Unfortunately, this risk is very real without ongoing monitoring and access to the latest data. As one analyst put it, the adage still holds: “garbage in, garbage out.” If an AI system is not properly maintained, regularly updated, and carefully guided, hallucinations and inaccuracies will inevitably creep in.

In higher-stakes applications, for example, financial services, healthcare, or travel, additional safeguards are often necessary. These could include human-in-the-loop verification steps, limitations on autonomous actions, or tiered levels of trust depending on task sensitivity. Ultimately, sustaining user trust in AI requires transparency. The system must prove itself to be reliable across repeated interactions. One high-profile or critical failure can set adoption back significantly and damage confidence not just in the tool, but in the brand behind it.

We’ve seen this before

We’ve seen this pattern before with algorithmic systems like search engines or social media feeds that drifted away from transparency in pursuit of efficiency. Now, we’re repeating that cycle, but the stakes are higher. We’re not just shaping what people see, we’re shaping what they do, what they buy, and what they trust.

There’s another layer of complexity: AI systems are increasingly generating the very content that other agents rely on to make decisions. Reviews, summaries, product descriptions – all rewritten, condensed, or created by large language models trained on scraped data. How do we distinguish actual human sentiment from synthetic copycats? If your agent writes a review on your behalf, is that really your voice? Should it be weighted the same as the one you wrote yourself?

These aren’t edge cases; they’re fast becoming the new digital reality bleeding into 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’s credible. But when AI begins to intermediate that feedback, intentionally or not, the ground starts to shift.

Trust as infrastructure

In a world where agents speak for us, we have to look at trust as infrastructure, not just as a feature. It’s the foundation everything else relies on. The challenge is not just about preventing misinformation or bias, but about aligning AI systems with the messy, nuanced reality of human values and experiences.

Agentic AI, done right, can make ecommerce more efficient, more personalized, even more trustworthy. But that outcome isn’t guaranteed. It depends on the integrity of the data, the transparency of the system, and the willingness of developers, platforms, and regulators to hold these new intermediaries to a higher standard.

Rigorous testing

It’s important for companies to rigorously test their agents, validate outputs, and apply techniques like human feedback loops to reduce hallucinations and improve reliability over time, especially because most consumers won’t scrutinize every AI-generated response.

In many cases, users will take what the agent says at face value, particularly when the interaction feels seamless or authoritative. That makes it even more critical for businesses to anticipate potential errors and build safeguards into the system, ensuring trust is preserved not just by design, but by default.

Review platforms have a vital role to play in supporting this broader trust ecosystem. We have a collective responsibility to ensure that reviews reflect real customer sentiment and are clear, current and credible. Data like this has clear value for AI agents. When systems can draw from verified reviews or know which businesses have established reputations for transparency and responsiveness, they’re better equipped to deliver trustworthy outcomes to users.

In the end, the question isn’t just who we trust, but how we maintain that trust when decisions are increasingly automated. The answer lies in thoughtful design, relentless transparency, and a deep respect for the human experiences that power the algorithms. Because in a world where AI buys from AI, it’s still humans who are accountable.

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