For a long time, retail was a clear process: a person had a need, searched for a product, compared offers and made a decision. Whether in a shop or later in an online shop hardly mattered. People were the centre of every purchasing decision.
This principle has changed dramatically in recent years. First through digitalisation, then through mobile commerce and finally through ever better personalisation. But despite all the technologies, one thing remained constant: the responsibility for the purchase lay with the customer.
This is exactly where the break begins.
Today, people search less and less themselves. They formulate an intention and leave the rest to technology. An AI researches, compares, evaluates options and makes a selection. The human sets the goal, the machine takes care of the journey. What used to be a shopping trolley is now an intelligent agent.
This change is more than just a new interface. It is changing who makes decisions in retail. And therefore also how retailers become visible, sell and grow.
Agentic commerce is not a trend, but a change of role
Many eCommerce trends have made great promises in the past. Voice commerce, augmented reality, virtual stores. Lots of attention, little lasting change. Agentic commerce is different because it does not enhance the experience, but shifts responsibility.
When a machine makes a selection, no campaigns, no emotional claims and no colourful banners count. The decisive factors are structure, reliability and clear rules. Machines make different judgements than humans. They act soberly. And that is precisely what makes this change so relevant.
In short:
Retail is no longer just optimising for buyers.
It optimises for decision-making systems.
Agentic commerce does not mean that people disappear from the process. But they take a step back. They delegate. They expect results instead of choices. This is convenient for buyers - and challenging for companies that have built their business model on visibility and traffic.
Before we delve deeper, it is therefore worth making a clear categorisation. After all, agentic commerce is still often misunderstood or confused with familiar AI approaches.
Agentic commerce definition: What is it all about?
Agentic commerce refers to a form of digital commerce in which AI agents independently prepare and execute purchase decisions on behalf of humans or systems. They research products, compare options, evaluate prices and availability and complete purchases independently within defined rules.
The crucial point is that these agents don't just react. They act.
This makes agentic commerce fundamentally different from traditional automation or chatbots, which only carry out predefined tasks or answer questions.
What characterises agentic commerce
An AI agent in the commerce context has three key characteristics:
- Autonomy: It works independently within clear guidelines.
- Contextual understanding: It takes goals, budgets, preferences and rules into account.
- Ability to act: It can use systems, fill shopping baskets and trigger purchases.
In other words, a chatbot helps you make decisions. An agent makes them.
Why this is more than just "smart AI in the shop"
Agentic commerce is not limited to individual touchpoints. The agent accompanies or takes over the entire customer journey. From recognising requirements to comparison and purchase. In many cases, it also goes beyond this, for example with repeat orders or service cases.
For retailers, this means a shift in perspective. The shop is no longer primarily a place for inspiration and persuasion. It is becoming a data and decision-making basis for machines.
Why waiting is not a neutral option
Companies that ignore agentic commerce don't lose sales overnight. But they gradually lose relevance. AI agents only access providers whose data, prices and rules they can clearly understand and evaluate. Those who are not compatible with this simply no longer appear in automated purchasing decisions.
This happens quietly. Without falling rankings, without warning messages in analytics tools. Decisions shift, while your own shop continues to look good. Only now, it no longer plays a decisive role.
Ruben Theerkorn
Head of Development & AI at elio GmbH
The new customer journey: when machines shop
The classic customer journey follows a familiar pattern. Generate attention, arouse interest, compare, convince, sell. This process is designed for people. With agentic commerce, this logic no longer applies.
Because the starting point is not a click, it is an intention.
Classic journey vs. agentic journey
How eCommerce used to work | How an agent journey works |
1. search or entry via marketing channels | 1. person formulates a goal or a need |
2. comparison of products and providers | 2. AI agent carries out research and evaluation |
3. decision based on content, design and price | 3. selection is based on clear criteria |
4. purchase in the shop checkout | 4. purchase is triggered automatically |
The difference is clear: humans no longer decide between options, but rather on framework conditions.
What agents really evaluate
An AI agent cannot be convinced. It checks. Soberly and consistently.
Relevant above all:
- Structured product data
- Price and availability
- Delivery times
- Return and purchasing rules
- Reliability of the source
Emotional arguments, visual stimuli or creative campaigns hardly play a role here.
Buying without a click
In agent journeys, the classic shop disappears as a visible location. The purchase takes place in the agent's interface or directly between systems. This feels unusual for retailers, but is logical.
As a result, retailers must not only be visible, but also capable of making decisions. And this is where the distinction we make in the next section becomes important. Because not every automated purchase decision is the same.
Categorised: Zero-click commerce does not mean that nobody buys any more. It means that purchasing decisions are automated.
Two worlds, two logics: Agentic commerce vs. machine-to-machine commerce
Up to this point, we have been talking about AI agents acting on behalf of humans. But this is precisely where it is worth making a clear distinction. Because not every machine purchase decision follows the same logic. If you mix them up, you will make the wrong decisions later on.
In essence, two parallel worlds of commerce are emerging.
Agentic commerce: humans set the direction
In agentic commerce, a person is still at the beginning. They formulate an intention, a goal or a need. The AI agent takes over the realisation. It researches, compares and makes a selection within clear guidelines.
Typical features:
Human target definition
AI takes over decision-making
Focus on convenience and speed
Often visible in chat or assistance interfaces
Humans remain decision-makers at a meta-level. The machine does the work.
Machine-to-machine commerce: processes buy themselves
Machine-to-machine commerce works differently. Here, there is no longer a human being at the moment of purchase. Systems communicate directly with each other. Orders are triggered because rules apply, not because someone formulates a need.
Typical features:
Fully automated processes
Fixed rules, budgets and approvals
Direct system and ERP connections
Particularly relevant in the B2B environment
Machines don't buy here because it's convenient, but because it's efficient.
Why this distinction is so important
Both worlds use AI. But they place completely different demands on shops, data and processes. Agentic commerce is changing the front end of retail. Machine-to-machine commerce is changing the rules of the game in the background.
Classification:
Agentic commerce optimises the experience.
Machine-to-machine commerce optimises efficiency and costs.
If you lump the two together, you end up optimising neither for people nor for machines. This separation becomes particularly relevant when looking at B2B commerce. This is where rules, budgets and automation meet ideal conditions for agent-based systems.
Why B2B is the natural starting point for agentic commerce
In B2C, agentic commerce often seems futuristic. Personal assistants, smart recommendations, shopping via chat. In B2B, on the other hand, it all feels surprisingly logical. Not visionary, but consistent.
Because B2B purchasing has always been structured. Budgets, approvals, supplier lists, framework agreements. These are precisely the conditions under which AI agents work particularly well.
B2B thinks in terms of rules, not emotions
While consumers make spontaneous decisions, B2B follows clear processes. Those who make purchases rarely do so out of desire, but out of necessity. It's about availability, price, delivery time and reliability. Perfect rules for machines.
Typical B2B patterns
- Recurring requirements
- Defined suppliers
- Fixed budgets and authorisations
- Clear specifications
An AI agent can not only map these rules, but also enforce them consistently.
Concrete agentic commerce use cases that already make sense today
Agentic commerce in B2B does not start with complex visions. It starts where processes are already standardised.
For example:
- Automatic reordering of MRO and C-parts
- Compilation of shopping baskets based on demand and contract
- Obtaining approvals before purchasing
- Optimisation of prices and delivery windows
No experimentation here. We optimise here.
Simon Neuberger,
CTO at elio GmbH
When agents negotiate with each other
A particularly exciting aspect arises when agents not only shop, but also interact with each other. Price enquiries, offer comparisons or delivery windows can be negotiated automatically. This is still often a pilot operation. But in standardised product groups in particular, this scenario is quickly coming within reach.
This shows that agentic commerce is not a promise for the future in B2B. It is the logical further development of existing digitalisation projects.
Agentic Commerce is already being built today - not just discussed
Agentic commerce would have remained a theoretical construct for a long time if one central question had remained unanswered: How does a machine actually pay? It is precisely at this point that a lot has happened recently.
Payment providers have started to think about AI agents as independent players. Visa, Mastercard and PayPal are working on solutions that allow agents to make payments without relinquishing control. The key lies in clear rules. Agents are not given blanket payment freedom, but work with defined budgets, limits and authorisations. Every transaction remains traceable and can be stopped at any time in case of doubt.
This is what makes automated shopping more than just an experiment. It becomes plannable. And above all: responsible.
Shop systems are opening up to machines
At the same time, the role of shop systems is changing. For a long time, they were built exclusively for humans. Navigation, filters, product pages. Now there is a second target group that thinks completely differently. Machines don't need interfaces. They need structure.
Platforms such as Shopify, SAP Commerce or Shopware are opening up their systems for precisely this access. Product data, prices and availability are provided in such a way that AI agents can retrieve them directly. In real time and without detours. The Model Context Protocol plays an important role here. It creates a standardised connection between agents and eCommerce systems and makes catalogues and shopping baskets machine-readable.
New touchpoints are emerging outside the shop
While the backend and payments are changing, the place of purchase is also shifting. Decisions are increasingly being made outside the shop. In chat interfaces, assistance functions or AI-supported search systems with integrated purchase options. The shop is not disappearing. But it is losing its role as a starting point. Reach is created where agents work. Not where banners hang.
These developments make it clear: Agentic Commerce will not happen at some point. It is being built right now. And anyone looking at it today will recognise that the course has long since been set.
Visibility rethought: Why SEO alone is no longer enough
When payment providers, shop systems and platforms open up their infrastructure to AI agents, this has a direct consequence: decisions are shifted to where these agents work. Visibility no longer arises automatically in your own shop or in traditional search results.
This particularly affects companies that previously equated visibility with traffic. Anyone who is not clicked on does not exist in this logic. In an agent-based world, this is too short-sighted. Sales are increasingly being generated without a direct visit to the shop.
What makes machines visible
AI agents do not follow any marketing logic. They are not looking for inspiration, but for reliable answers. The criteria according to which visibility is created are changing accordingly.
The decisive factors are above all
- Consistent and structured product data
- Clear prices and availability
- Clear delivery and return rules
- Reliable sources and systems
- Clean technical connection
Those who convince here are considered. Anyone who is unclear disappears from the selection.
From rankings to relevance
For a long time, visibility in eCommerce was a question of ranking. If you were at the top, you were clicked - and if you were clicked, you had a chance to sell. Search engine optimisation (SEO) was the key tool for this. However, this model only works as long as people compare and select for themselves. This is precisely where traditional SEO loses its impact, as decisions are increasingly being made directly in AI responses rather than in search results.
Many new terms are currently emerging in this context. GEO, AEO, GAIO. They all try to describe the same phenomenon: Preparing content and data so that it can be correctly understood and utilised by generative systems such as ChatGPT, Gemini or Perplexity. The name is of secondary importance. The mechanism is crucial.
AI systems do not provide hit lists. They formulate answers. They combine information, evaluate sources and make preselections. To do this, they only use content that appears clear, consistent and trustworthy. Everything else falls through the cracks, regardless of how well it ranked in the past.
As a GEO agency, we support companies in optimising their content, product data and structures specifically for generative search systems and AI agents.
The difference to classic SEO is fundamental. SEO competes for attention. These new optimisation approaches are competing for reliability. If you get too hung up on terms, you will miss the real change. The decisive factor is not what you call it, but that you understand how decisions will be made in the future.
Volker Riedel,
Head of Marketing at elio GmbH
The shop of the future is a system node
The previous sections focussed on how agents make decisions. Now it's about the other side of the coin: what role the shop still plays in this world.
The short answer: no longer a stage, but an infrastructure.
For AI agents, the shop is not a place you visit. It is a system that is accessed. And it is targeted, automated and in real time.
From the front end to the access point
We have already seen what agents are interested in: Data, prices, availability, rules. The decisive factor now is how this information is provided.
The shop becomes the technical hub between:
- Product and catalogue data
- Stocks and prices
- Logistics and return policies
- Payment and release logics
Not as a pretty interface, but as a reliable source.
Design remains relevant for people. Not for machines. Agents do not need product detail pages, but clearly structured information that they can compare and categorise. This brings technical basics to the fore, such as somewhat structured data (Schema.org), clean APIs with real-time access or consistent data models across all systems. An agent can only evaluate products in a meaningful way if this basis is right.
One system, two target groups
The shop of the future serves two very different customers. People want orientation and trust. Machines want structure, stability and access. Enabling both at the same time is not a design issue, but an architectural decision.
Companies that already think of their shop as a system node are creating precisely this basis. All others continue to optimise for an interface that is less and less the place where decisions are made.
Trust, control and governance: why agentic commerce fails without clear rules
The more autonomously systems act, the more important one question becomes: who retains control? Agentic commerce thrives on speed and automation. But without trust, it will not become a viable model. Neither internally nor towards customers.
Precisely because AI agents can buy independently, they need clear guard rails. Not at some point. But right from the start.
Autonomy needs limits
Agents are allowed to make decisions, but not everything. Budgets, authorisations and responsibilities must be clearly regulated. Otherwise uncertainty arises. And uncertainty is the quickest way to cancel a project.
In practice, this means that an agent is allowed to make purchases, but only within a defined framework. It adheres to price limits, supplier lists or approval processes. Decisions are prepared automatically, but not executed blindly. This creates trust among all those involved.
Transparency instead of a black box
Another success factor is traceability. Companies must be able to understand why an agent has done something. What data it has used. Which rules have been applied. Without this transparency, efficiency quickly turns into a loss of control.
Modern agent architectures therefore rely on monitoring and logging. Activities, decisions and costs can be viewed centrally. Not as monitoring, but as a control instrument.
Governance is not an IT discipline
A common mistake is to think of governance exclusively in technical terms. In reality, it is organisational. Agentic commerce affects purchasing, IT, eCommerce, legal and compliance in equal measure. Rules must be jointly defined and supported.
Companies that create clear responsibilities early on make faster progress. Not because they have fewer risks, but because they deal with them better.
How should the use of AI agents in eCommerce be categorised under the EU AI Act? Our blog post will tell you.
Trust as a competitive advantage
In the end, trust determines whether Agentic Commerce is used productively. Both internally and externally. Customers, partners and systems must be able to rely on agents acting in a comprehensible and compliant manner.
Those who think of governance as an integral component not only gain control. They gain speed. And that is precisely what makes the difference between experimentation and real progress.
What companies should do now - concrete and pragmatic
Agentic commerce is not decided in strategy meetings, but in everyday life. In data models, processes and responsibilities. If you want to do something today, you don't have to reorganise everything. But they must start in the right places.
1. check product and price data honestly
The first lever is always the database. Not in theory, but in practice. Are prices and availability correct across all channels? Are product attributes complete and consistent? Are there clear rules for delivery times and returns?
What "somehow fits" for people is no longer enough for agents. Contradictions lead to offers being ignored.
2. consider machines as a target group
Many shops are neatly optimised for humans, but invisible to systems. This is exactly where a change of perspective is worthwhile. Can external systems retrieve your data in real time? Are interfaces stable and documented? Is it clear which information an agent is authorised to use and which is not?
If you cannot answer these questions, you will not take part in agent journeys.
3. select a clear use case
Agentic commerce does not start in marketing, but in processes. Areas with clear rules and little emotion are particularly suitable. Repeat orders, standard products, MRO, spare parts. Agents can be tested here without jeopardising the core business.
A clear framework is important: What is the agent allowed to do? Where does his responsibility end? How is it controlled?
4. consider governance from the outset
Agents need approvals, budgets and limits. Not later, but right from the start. Those who postpone governance slow themselves down. Those who integrate it create trust internally and externally.
This is crucial for acceptance, especially in the B2B environment.
5 Don't wait for terms, look for impact
Whether agentic commerce, GEO or machine-to-machine. The terms will change. The logic remains the same. Machines make decisions based on data. Those who ensure that this data is reliable, accessible and controllable today are prepared.
Simon Neuberger,
CTO at elio GmbH
Conclusion: Agentic commerce is not a trend, but a change in perspective.
Agentic commerce is not only changing how people shop. It is changing who makes the decisions. Machines are taking over tasks that were previously performed by humans. They compare, evaluate and purchase. Objectively, rule-based and consistently.
For companies, this means one thing above all else: the online shop is no longer just a sales platform. It is becoming infrastructure. A system that is used by humans and machines. Those who focus solely on design, campaigns and classic SEO logic are optimising beyond reality.
Agentic commerce requires clean data, clear rules and an architecture that allows for automated decisions. Not sometime in the future, but now. Because the infrastructure is already emerging, touchpoints are shifting and the first purchasing decisions are already being made today without the classic click.
Companies that understand this early on gain room for manoeuvre. All others react later under pressure.
Implementing agentic commerce - not just talking about it
This is exactly where we at elio come in. We support companies in making their shops and commerce architectures agentic ready. From data and system analysis to optimisation for GEO and agentic touchpoints through to technical implementation. We have been the go-to partner for complex B2B projects for over 25 years.
Not as a buzzword consultant, but as a digitalisation and eCommerce partner with a focus on B2B. Pragmatic, structured and focussed on what really works - talk to us.
Those who tackle this change of perspective now are helping to shape it. Everyone else will be shaped.
