Agentic commerce runs on truth and context
Foto: MIT Tech Review
As much as 80% of decision-making processes in modern trade could soon be taken over by autonomous AI systems, heralding the arrival of the Agentic Commerce era. In this new model, it is not the human, but an intelligent agent—possessing full context of our preferences, budget, and purchase history—that will independently search, negotiate, and finalize transactions. The foundation of this revolution is the transition from simple chatbots to advanced Agentic AI systems that operate on "truth and context," eliminating information noise and manipulative marketing techniques. For users, this means radical time savings and an end to the tedious comparison of hundreds of offers. The agent will not only find the product but, thanks to access to real-time data, will verify its authenticity and actual utility value. However, the key challenge remains the integration of dispersed data and ensuring security in machine-to-machine relations. Companies that fail to adapt their infrastructure to cooperate with digital proxies risk invisibility in an ecosystem where purchases are decided by precise algorithms rather than flashy advertising. Therefore, the success of Agentic Commerce depends on building radically transparent communication channels between the brand and autonomous software.
Imagine that instead of spending hours scrolling through booking portals, you tell a digital assistant: "Use my loyalty points and book a family trip to Italy. Stay within the budget, choose hotels we liked before, and take care of all the details." In the world of agentic commerce, the assistant doesn't come back to you with a list of links to choose from—it independently creates an itinerary and finalizes the transaction. This fundamental shift, from assistance to execution, defines a new era of commerce driven by autonomous AI agents.
The transition from traditional e-commerce to agentic models is not just an evolution of the interface, but a complete redefinition of the purchasing process. As MIT Technology Review notes, the foundation of this change is trust based on two pillars: truth and context. For an agent to act on our behalf, it must have access to precise product data and a deep understanding of our personal preferences, purchase history, and financial constraints.
From search to autonomous execution
The current online shopping model is based on "search and click." The user is the information processor—they must filter reviews, compare prices, and check availability. Agentic AI reverses this dynamic. The agent becomes a proxy operating within the commercial ecosystem, making decisions based on parameters defined by the human. The key difference here is agency: the system not only suggests what to buy but has the authority to execute payment and confirm the order.
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However, introducing autonomy into commerce requires solving the problem of "hallucinations" in language models. In agentic commerce, there is no room for errors in price or product specifications. These systems must rely on real-time data structures that guarantee every piece of information the agent relies on is current and true. Without absolute certainty regarding facts, autonomous commerce will remain merely a technological curiosity rather than a real business tool.
Context as fuel for digital proxies
The truth about a product is only half the battle; the other half is the user's context. For an agent to effectively plan the aforementioned trip to Italy, it must know more than just which hotels are available. It must understand that "hotels we liked" means boutique properties with pool access, not chain high-rises in the city center. Context includes:
- Transaction history: Analyzing previous choices allows for precise offer matching without the need to ask additional questions.
- Loyalty programs: Optimizing costs through intelligent management of points and discounts, which is often a tedious and opaque process for a human.
- Budget constraints: Rigid financial frameworks that the agent must adhere to while negotiating or selecting alternative options in real-time.
- Personal preferences: From food allergies to favorite airlines – details that build purchase satisfaction.
The integration of this data allows for the creation of a personalized shopping profile that is dynamic and evolves with the user. In this model, the agent is not just a sales bot for a given brand, but rather a personal curator that filters the global market through the prism of individual customer needs.
Challenges for brands and data infrastructure
For technology companies and retailers, the emergence of agentic commerce means the necessity to rebuild information architecture. Traditional websites, optimized for the human eye and SEO algorithms, may prove unreadable for autonomous agents. The future belongs to APIs and structured databases that will allow machines to communicate instantly and exchange information about inventory levels or technical specifications.
Agentic commerce is not about systems talking to us more. It is about systems doing more while talking to us less. It is a transition from conversation to conversion without unnecessary intermediaries.
Another aspect is the issue of security and authentication. If an agent is to make purchases, there must be a secure protocol for delegating payment authority. Technologies such as tokenization and biometric confirmation of intent will be crucial to prevent unauthorized transactions and ensure users maintain control over their digital wallets.
A new hierarchy of trust in technology
In traditional commerce, we trust the brand or the platform. In agentic commerce, we must trust the AI's decision-making process itself. This requires transparency regarding why the agent chose a specific option. Did it do so because it was best for the user, or because it received a higher commission from the provider? Agent neutrality will become one of the most important topics of debate in AI ethics in the coming years.
Instead of being flooded with thousands of options, the consumer will receive one, perfectly tailored proposal. This radically reduces "decision fatigue," but simultaneously places enormous responsibility on software developers. Commerce based on truth and context is a promise of efficiency that will change how we exchange money for goods and services, making AI technology a real participant in the economic market.
It can be assumed that in the near future, having one's own digital representative will become the standard—one that will manage our subscriptions, negotiate telecommunications service prices, and plan the logistics of daily life. The winners in this new economy will be those entities that most quickly provide agents with reliable data, moving away from the fight for human attention in favor of precise, machine-to-machine cooperation.
