As digital commerce becomes more complex behind the scenes, two architectural approaches are emerging as critical to staying scalable and competitive: composable commerce and agentic commerce. Both signal a shift away from rigid, monolithic platforms, but they solve fundamentally different challenges.

Composable commerce focuses on how systems are structured. It promotes modularity, allowing individual services like product information, pricing, and fulfillment to be independently managed, updated, or replaced. The goal is to enable faster innovation and greater flexibility by decoupling components across the commerce stack.

Agentic commerce, on the other hand, is concerned with how systems behave. It introduces AI agents that can make decisions, act autonomously, and coordinate execution across workflows in real time. As we explored in a recent piece by Slashdot, agentic models move beyond AI as an assistive layer, shifting toward autonomous execution across both frontend and backend domains.

Understanding how these two approaches interact is key to designing future-ready commerce architectures. In this article, we’ll cover:

• What is composable commerce and how it enables modular, API-first system design
• What is agentic commerce, and how AI agents bring autonomous execution into commerce ops
• Key differences between composable and agentic commerce, including structure, orchestration, and system behavior
• How composable and agentic models work together in enterprise environments
• What to look for in agentic commerce platforms, including orchestration, governance, and real-time decisioning

What is Composable Commerce?

Composable commerce is an architectural approach that structures the digital commerce stack into modular, interchangeable components. Instead of relying on a single, tightly coupled platform to manage everything from catalog to checkout, composable systems separate these functions into discrete services such as PIM, OMS, pricing, and CMS that interact via APIs.

Composable commerce enables agility through architecture, giving organizations the freedom to compose, recompose, and extend digital capabilities without replatforming.

This model is closely aligned with the MACH architecture (Microservices, API-first, Cloud-native, and Headless), which promotes scalable, decoupled, and cloud-optimized systems. By adhering to these principles, organizations gain greater flexibility to adopt best-of-breed vendors, evolve individual services independently, and respond more quickly to market demands. That said, composability doesn’t need to follow a rigid MACH prescription. Many enterprises evolve their architectures in phases, combining packaged solutions with modular extensions and layering services as needed. The strength of composable commerce lies in its adaptability, not just its purity of design.

Pairing composable architecture with low-code orchestration introduces further advantages. As explored in Streamlining Ecommerce with Low-Code, low-code platforms allow teams to define and adapt business logic visually, reducing reliance on custom code while improving collaboration across technical and non-technical roles. This makes it easier to govern interactions between services, manage exceptions, and accelerate implementation timelines.

While composable commerce improves system flexibility, it still relies heavily on predefined rules, manual configurations, or developer-authored logic to coordinate behavior across services. The system’s structure may be modular, but its ability to act remains limited to what has been explicitly programmed. It can’t reason through exceptions, adapt to real-time conditions, or make decisions on its own. This is where agentic commerce extends the model: by bringing context-aware, autonomous execution into modular environments, enabling systems not just to integrate flexibly, but to operate intelligently and independently across those integrations.

What is Agentic Commerce?

Agentic commerce refers to a new layer of intelligence within digital commerce systems that enables autonomous AI agents to act on behalf of the business. These agents aren’t just optimizing user experiences or suggesting content. They are embedded actors that can make decisions, execute tasks, and coordinate workflows across both frontend and backend operations.

Agentic commerce is the application of AI agents that execute real-time decisions within and across composable systems, not as assistants, but as operational actors.

As detailed in Agentic Commerce with AI Agents, the defining shift is one from assistance to execution. Traditional AI in ecommerce has focused on support: recommending products, flagging risks, or surfacing insights. Agentic commerce expands that role, allowing agents to reason through operational context and take action, whether it's enriching product data, rerouting an order, or resolving an exception in real time.

Unlike rule-based automation or rigid workflows, agentic systems are context-aware. They evaluate current conditions, adapt to business constraints, and escalate only when needed. Their behavior is governed, but not hard-coded, enabling more flexible and scalable operations across dynamic environments like marketplaces, B2B catalogs, or global fulfillment networks.

Agentic commerce doesn’t replace composable systems; it activates them. By giving services the ability to reason, act, and collaborate autonomously, agentic logic turns a modular architecture into a living, responsive ecosystem that can operate with speed, intelligence, and governance at scale.

Key Differences Between Composable and Agentic Commerce

Composable commerce and agentic commerce serve fundamentally different, but highly synergistic, purposes. Composable commerce provides the structural architecture needed to support modern commerce systems, while agentic commerce layers on intelligent automation that executes within those structures.  In practice, they work best together: composability lays the groundwork, and agentic logic makes it operationally scalable.

Here’s a practical breakdown of how the two approaches differ:

In short, composability gives you modular control over what your system can do. Agentic commerce gives your system the intelligence to know when and how to do it. The result isn’t just fewer manual steps, it’s an operational model that scales decision-making itself, not just service modularity.

Take PIM as an example. In a composable setup, product validation, categorization, and enrichment might each be handled by separate services. But coordinating them typically involves business rules, manual review, or integration logic written by developers. With agentic commerce, an embedded agent evaluates vendor data, fills in missing attributes using contextual reasoning, and flags exceptions without needing orchestration flows to be hardcoded. This shift, as explored in Reimagining PIM with Low-Code AI Automation, improves time-to-listing while reducing the burden on ops teams.

A similar pattern emerges in marketplaces. Composability enables independent modules for vendor onboarding, SLA enforcement, product data, and fulfillment. But integrating logic across these domains, especially under variable conditions, requires either manual oversight or complex scripting. With agentic execution, agents coordinate across those modules directly, rerouting orders, resolving inconsistencies, and escalating when policies are breached. This is outlined in our Scalable Marketplace Orchestration Playbook, where agents drive operational decisions without bloating team workloads.

Why Composable Platforms Alone Aren’t Agentic-Ready

Composable commerce has significantly improved how digital systems are structured. By decoupling capabilities across modular services, such as search, promotions, product information, and fulfillment, it enables teams to iterate and integrate faster, especially when using low-code tools. These platforms are built to be flexible, API-first, and integration-friendly.

While composability improves what a system can do and how services are arranged, it doesn’t inherently define how decisions are made or actions are executed.

Most composable platforms still rely on manual scripting, static rule definitions, or orchestrated workflows that must be updated when conditions change. The system remains dependent on external logic to coordinate operations, especially across domains.

This gap becomes more visible as enterprises aim to scale decision-making, not just services. That’s where agentic commerce enters. It introduces autonomous, context-aware agents that can evaluate conditions, trigger flows, and act across services, without needing every decision to be hardcoded or pre-orchestrated.

To support agentic commerce, composable platforms must evolve beyond modularity and integrate three foundational capabilities:

• Context-aware decision engines: These allow agents to assess live conditions, such as SLA risks, incomplete product data, or regional constraints, and determine the appropriate next step. Rather than executing predefined paths, the system reacts to context and logic embedded in the agent.
• Middleware for real-time orchestration: Agents must be able to coordinate across APIs and services in real time, without being tied to batch jobs or rigid pipelines. As explored in MCP Middleware and Beyond for Enterprise Agent Execution, this requires execution-first middleware capable of routing agent behavior across composable domains with full observability.
• Governance interfaces for controlled autonomy: Execution at this level needs oversight. Business users must be able to define constraints, manage escalation paths, and trace agent decisions. Platforms that support agentic execution treat autonomy as a governed capability, not just an engineering challenge. This is unpacked further in Designing Systems for AI as a User.

Composable platforms can certainly integrate intelligent automation and even predictive models. But to enable agentic execution, they must support dynamic behavior, distributed coordination, and governed autonomy at the execution layer.

Evaluating Platform Readiness for Agentic Commerce

As the operational demands of commerce grow more complex, many platforms are beginning to offer AI-powered features. But not all of them are built to support agentic execution, where autonomous agents can act independently, coordinate across services, and operate within enterprise-level guardrails.

Evaluating platform readiness for agentic commerce requires looking beyond marketing claims to understand the underlying architecture and governance model. Here are five key questions to guide that evaluation:

1. Do agents operate natively or as bolt-on services?
   Native agent execution allows AI agents to run within the same orchestration environment as core services like PIM, OMS, and fulfillment. Bolt-on approaches often require external triggers or integrations, limiting real-time responsiveness and creating governance blind spots.

2. How is execution logic defined, versioned, and governed?
   Agent behavior should be configurable through policies or flows, not buried in opaque code or external scripts. Look for platforms that support versioning, rollback, and change management for execution logic, ideally with transparency for both technical and business stakeholders.

3. Is there a control layer for managing agent autonomy?
   Agentic execution shouldn’t be a black box. Business users need visibility into what agents are doing, when they escalate, and how exceptions are handled. A low-code control layer helps enforce thresholds, approvals, and decision constraints while making governance collaborative.

4. Can agents coordinate across domains (Agent-to-Agent or A2A coordination)?
   Many real-world scenarios require agents to work together. For example, a fulfillment agent might need to consult a pricing agent before rerouting an order. Platforms must support this kind of multi-agent orchestration across domains, without creating brittle dependencies.

5. How is observability handled?
   Traceability is critical when autonomous systems are involved. The platform should provide clear audit trails for agent actions, including inputs, decisions made, and resulting outcomes. This ensures operational trust and supports continuous improvement.

These questions are especially important when evaluating vendor claims about AI capabilities. As explored in Building Empowered AI Agents for Enterprise, true agentic commerce requires more than intelligent plugins. It requires an execution model that is designed for autonomy, governed by policy, and capable of adapting in real time across the commerce stack.

Getting Started with Rierino Agentic Commerce

By now, it’s clear that composable and agentic commerce address different, but deeply connected, layers of modern commerce architecture. Composability brings flexibility through modular service design. Agentic commerce adds a new dimension: intelligent execution that scales decisions and coordination across those services.

For most teams, the natural next question is: Where do we begin?

The starting point isn’t to replace what you already have. It’s to assess your current architecture’s readiness:

• Are your systems already modularized into distinct domains (PIM, pricing, fulfillment, etc.)?
• Do you have orchestration layers that define how services interact?
• Are your workflows still dependent on rule-based automation or custom scripts?

Once you understand where decision-making and execution are bottlenecked, you can begin layering in agentic capabilities incrementally, and with control.

At Rierino, we’ve designed our platform to support this evolution from the ground up:

• Composable modules define clear execution domains like PIM, pricing, search, and content
• AI agents operate natively within those domains, not as external add-ons
• Low-code orchestration tools allow teams to define flows, policies, and escalation paths without writing custom logic
• Agent-to-agent coordination (A2A) makes it possible to automate cross-domain workflows while maintaining governance and observability

Together, these capabilities allow teams to build systems that are both structurally agile and operationally intelligent, capable of executing with autonomy, without sacrificing control.

Want to get started? Explore Rierino Commerce, learn more about AI Agent Builder, or get in touch with our experts right here and now.