AI is already an integral part of the ecommerce experience, from personalizing search to tailoring recommendations and optimizing campaigns. These use cases have all delivered measurable impact and continue to evolve. However, a new shift is underway that is expanding AI’s role across the entire customer journey, from discovery to delivery and beyond.
This emerging approach is known as agentic commerce, where AI agents for ecommerce take on active, autonomous roles within commerce systems. Their function goes beyond enhancement to execution: orchestrating backend processes, automating decisions, and adapting to context in real time. It’s a move from insight to action, and from isolated automation to intelligent coordination across the commerce stack.
And the urgency to adopt such capabilities is growing. A recent Digital Commerce 360 survey found that over 44% of ecommerce leaders are already seeing strong results from AI, while only 11% have no immediate plans to invest in it. In contrast, interest for technologies like voice commerce is declining, signaling a shift in focus toward executional impact and operational scalability.
In this article, we explore how agentic commerce is shaping the next phase of ecommerce. We’ll unpack what defines this model, where it creates the most value, and how enterprises can build toward systems that not only scale but think and act on their own within organizational boundaries.
AI has made significant inroads into ecommerce from product recommendations and search optimization to fraud detection and supply forecasting. But most of these implementations take on a more assistive role of helping users, informing decisions, and suggesting improvements. Now, agentic commerce takes this further by bringing AI into the engine room.
Agentic commerce is the use of autonomous AI agents that can act independently within digital commerce systems, making decisions, executing tasks, and adapting to context across both front-end and back-end workflows. Also referred to as A-Commerce, this model is gaining traction as businesses seek more adaptive, execution-first AI and intelligent automation capabilities.
Agentic commerce isn’t just AI that recommends — it’s AI that runs parts of your business.
In agentic commerce, agents do more than suggest or predict, they take action. From onboarding new vendors and enriching product data to rerouting orders and escalating service issues, agentic systems help coordinate the moving parts of modern commerce with speed and intelligence. The big difference lies in execution. While traditional automation relies on rigid rules or custom logic, agentic systems can evaluate, prioritize, and adapt in real time following contextual triggers rather than static workflows.
Agentic commerce enables businesses to scale intelligently, reducing manual effort while increasing operational agility. It’s not just the next step for AI in commerce. It’s a structural shift in how modern digital businesses are run. At Rierino, we’ve embedded this approach in our AI Agent Builder, enabling enterprises to create agents that operate within a governed, low-code environment. These agents are not bolt-ons, they are integrated actors in composable systems, orchestrated with visibility and control.
Commerce has never been more sophisticated or more operationally demanding. While digital experiences have evolved, the systems behind them haven’t always kept pace. Agentic commerce is gaining traction now because it directly addresses the pressure points modern commerce operators face, especially at scale.
1. Complexity is rising faster than automation can adapt
From multi-vendor marketplaces to region-specific promotions and SLA-based fulfillment, today’s operations demand flexibility. Traditional automation, with its hard-coded rules, one-off scripts, and rigid process flows, simply doesn’t scale across this growing complexity. Agentic commerce introduces intelligence where it’s most needed: in the execution layer. Instead of expanding teams or technical overhead, AI agents have the ability to step into logic-heavy workflows and act on behalf of systems.
2. Manual intervention is still the hidden cost of scale
Despite advances in ecommerce tooling, many operational workflows still depend on human oversight. Teams step in to reprice products, correct vendor data, resolve exceptions, or approve out-of-policy fulfillment decisions. Agentic systems reduce this load by acting contextually and independently without needing constant human hands on every operational lever. This frees up teams to focus on strategy instead of maintenance.
3. Generative AI has raised the bar for enterprise expectations
As large language models (LLMs) and generative interfaces become more visible across business functions, enterprises are asking a new question: What can AI do beyond content generation? Agentic commerce provides the answer, moving from passive outputs to active execution. As outlined in Building Empowered AI Agents for Enterprise, agents can now operate with real autonomy, context awareness, and governance. They don’t just generate, they coordinate, act, and adapt across real-world commerce logic.
4. Operational decisions now need to happen in real time
Speed has always mattered in commerce, but what’s changed is where that speed is required. It’s no longer just about site performance or fast delivery. Decisions like routing an order, adjusting inventory visibility, or enforcing a time-sensitive promotion now need to happen in the moment, not in batch jobs or backend queues. Agentic commerce enables this shift by embedding logic into agents that operate contextually. They act across systems such as OMS, PIM, pricing, or customer service in real time, ensuring that business logic keeps pace with customer behavior.
5. Low-code and composable stacks make execution more accessible
What used to require deep custom integration and hard-coded workflows can now be orchestrated through low-code commerce platforms and modular services. Agentic systems thrive in this environment, acting within defined domains and communicating across APIs without rebuilding your stack. This unlocks execution-level intelligence without sacrificing time-to-market or technical governance. It’s how adaptive operations become sustainable.
Low-code tooling and composable platforms haven’t just made commerce systems more modular, they’ve laid the groundwork for intelligence to operate more fluidly across them. This shift is what makes agentic execution not only feasible but also sustainable.
In traditional monolithic environments, agents would struggle to operate independently. Each system is tightly coupled, logic is deeply embedded, and even minor updates require developer intervention. But in composable ecosystems, services are decoupled by design, giving agents the clarity and control to act within specific domains without disrupting the whole.
This is where composability meets backend orchestration. Each domain, whether it’s PIM, pricing, content, or fulfillment, becomes a discrete execution surface. Agents can be deployed to manage the logic of that domain, communicate with others via APIs, and adapt to changes without triggering a full-stack ripple effect.
Agentic commerce relies on clear system boundaries, and composable architecture makes those boundaries executable.
For example, a product onboarding agent might operate within a PIM service to validate metadata and enrich attributes, while a procurement intelligence agent evaluates recent supplier quotes and contract terms to suggest optimal reorder quantities or identify pricing inconsistencies. These agents don’t need to know the entire system, they just need a well-defined lane to operate in and a way to communicate context.
Low-code orchestration layers make this even more powerful. They allow teams to define, govern, and visualize how agents interact with services and each other without hard-coding the logic behind every path. This accelerates time-to-value and reduces long-term complexity. As explored in Streamlining Ecommerce with Low-Code, visual orchestration isn’t just about speed. It’s about creating a shared interface where business, product, and technical teams can collaborate around execution and where agents can operate under clear rules, constraints, and approvals.
In a composable world, agents are no longer disruptive. They’re additive. And more importantly, they’re aligned with domain boundaries, with enterprise governance, and with the speed modern commerce demands.
As agentic commerce shifts AI from passive insights to active execution, the stakes grow. An agent adjusting prices, rerouting orders, or overriding inventory rules brings real power but also real risk. What happens when conditions change? Who approves the action? How do you trace decisions back to business intent?
Without a control layer, autonomous agents can become opaque, unpredictable, and difficult to govern, especially at scale.
This is where low-code orchestration becomes essential. Not just for building workflows, but for managing intelligent behavior safely and transparently. A low-code interface makes agent logic visible, changeable, and accountable without burying it in code or requiring custom integrations to control it.
Enterprise operations can’t rely on trial-and-error autonomy. Business stakeholders need a way to define rules, set thresholds, apply approvals, and trace outcomes — all in a platform that supports collaboration across product, operations, and engineering.
With low-code orchestration:
As explored in Low-Code for Sustainable Commerce, sustainable systems are built not just for agility, but for longevity and trust. When agents are configured in a low-code environment, teams can continuously refine their behavior without increasing system fragility or developer dependency.
At Rierino, low-code orchestration is designed to act as a governance interface for agents. You can define who the agent is, where it operates, under what conditions, and with what level of autonomy. It’s how we ensure that intelligence doesn’t come at the expense of oversight and how agentic commerce stays aligned with business intent.
Agentic commerce is more than a technical shift, it's a practical response to operational friction. That’s why it isn’t tied to a single use case, but represents a new operational layer, adaptable wherever systems struggle to scale intelligently.
Whether you're managing a large-scale marketplace, a direct-to-consumer brand, or a complex B2B catalog, the pain points are often the same: workflows that don’t scale, logic that doesn’t adapt, and teams overwhelmed by manual coordination. While much attention has been given to AI agents that assist consumers directly (e.g. browsing and buying on their behalf), the greater opportunity for many enterprises lies behind the scenes: agents that drive real-time decisions, orchestrate backend workflows, and reduce operational friction across teams.
Here's what agentic commerce looks like in action across real enterprise environments:
For marketplaces and B2B platforms, onboarding new vendors can be slow, manual, and error-prone. Product data often arrives incomplete or inconsistent, delaying listings and adding overhead to merchandising and catalog teams. AI agents can validate and enrich product information, detect taxonomy mismatches, and enforce listing standards automatically. They surface only true exceptions for review, reducing the need for manual back-and-forth during onboarding.
Rierino’s agentic architecture allows agents to operate directly within PIM or content modules, with full awareness of listing rules, category logic, and vendor-specific nuances. Composable domains and escalation rules are built in so onboarding stays fast, clean, and governed.
As fulfillment networks grow more complex, routing decisions often need to account for inventory, cost, delivery windows, partner SLAs, and regional constraints. Manual routing or brittle rules don’t keep pace. Agents can dynamically assess current conditions such as stock levels, location, or SLA risk and decide the optimal routing path. They adjust in real time and escalate when tradeoffs require human input.
Rierino’s execution-first agents are embedded within the orchestration layer. That means they can evaluate conditions across OMS, inventory, and shipping modules in a coordinated way while remaining visible, versioned, and governable through a low-code interface.
Exceptions, like a pricing mismatch, delivery delay, or missing metadata, are inevitable. But most systems either block the process or require manual resolution, leading to inefficiencies and delays. Agents can detect and triage exceptions in real time. They can retry operations, auto-correct based on business rules, or escalate with full context and recommendations, keeping workflows moving without compromising accuracy.
Rierino agents are deeply contextual and policy-aware. They operate within defined parameters, but with the flexibility to reason through logic trees, escalate edge cases, and adapt to operational context. Governance logic ensures that any escalation, correction, or bypass is tracked and auditable, giving teams confidence without creating friction.
In B2B commerce, product recommendations aren’t about impulse buys, they’re about functional fit. Procurement teams need to ensure that what they’re buying aligns with existing systems, standards, or workflows. Yet, most recommendation systems still operate on basic correlation, ignoring compatibility, specs, or contextual requirements. Agentic systems powered by LLMs can bridge this gap. Instead of just recommending products that are frequently bought together, agents can reason across technical attributes, usage context, and procurement history to suggest what should be purchased together. For example, when a buyer selects an industrial sensor, the agent can recommend compatible mounting kits, calibration tools, or approved power adapters, not just based on SKU matching but by understanding technical specifications, operating environments, and past procurement logic.
With Rierino’s composable product and content modules, agents can work across PIM, account-specific pricing, and purchase history. They don’t just surface complementary products, they tailor suggestions based on customer type, regulatory requirements, or regional standards. As outlined in Revolutionizing B2B Ecommerce, modern B2B buyers expect the same seamlessness as B2C, but with far more operational weight behind each purchase. Agentic commerce enables intelligent, explainable recommendations that align with business logic, reducing misorders, improving customer satisfaction, and increasing average order value without guesswork.
The post-purchase phase often lacks automation, even though it’s critical to customer satisfaction. Return approvals, refund logic, and post-shipping communication are frequently siloed or manual. Agents can initiate returns based on policy, track fulfillment success, update systems in sync, and communicate with customer service channels, reducing both delays and confusion.
With composable modules and low-code control, Rierino allows agents to operate seamlessly across post-purchase touchpoints. Business teams can monitor flows, set override logic, and adapt rules, turning post-purchase into a proactive experience layer.
What unites these examples is simple: agents take action, not just alert. They don’t replace teams — they make them more scalable, responsive, and focused. Whether it's a B2C flash sale, a B2B contract renewal, or a new vendor onboarding, agentic commerce ensures your business logic is applied exactly when and where it's needed.
AI is no longer just supporting commerce, it’s ready to run it. Agentic commerce represents this shift, where execution demands systems that are both intelligent and accountable. At Rierino, this is already built into the foundation of our platform:
Composable domains. Execution-first agents. Low-code orchestration. Enterprise-grade governance.
This foundation gives teams the ability to bring AI into the core of their operations, not as a bolt-on, but as a fully integrated layer of decision-making and action. Agents built with Rierino operate within clear boundaries, respond to real-time context, and scale without introducing fragility. More importantly, they do so in ways that align with how your systems and teams already work.
As discussed in Designing Systems for AI as a User, realizing this potential means designing platforms where agents can operate responsibly, adapt to complexity, and contribute meaningfully. Agentic commerce is not about replacing teams, it’s about equipping systems to think and act at the speed modern business requires. And with the right foundation, that shift doesn’t need to be a leap. It can start now with one agent, one domain, and one decision at a time.
Want to get started with agentic commerce? Get in touch to explore how Rierino can help you build intelligent agents that act with purpose, operate with control, and scale with your business.
