GenAI-First Agentic Ecosystems Are Driving a Composable Commerce Transformation

Legacy commerce platforms and ecosystems often appear robust but are increasingly found to be rigid, especially when facing the pressure of modern retail demands. Over time, monolithic tech stacks evolve into complex, tightly coupled systems. Functions like e-commerce, inventory, warehouse and catalog management, promotions, checkout, loyalty programs, and content all become deeply interwoven in a single digital commerce ecosystem.

Such complex architectures reduce business agility and make it difficult to meet consumer needs across platforms. Even simple updates become complicated and risky to implement. Across retail enterprises, technology teams face delays due to integration challenges, and continued investment in these monoliths deepens dependencies, isolates data, and slows innovation.

To overcome these limitations, retailers are exploring modern approaches built for agility and speed. MACH architecture, which refers to microservices, API-first, cloud-native, and headless, has redefined how modern digital commerce systems are structured. It has enabled the rise of composable commerce, an ecosystem built from modular and interchangeable components that work seamlessly across key retail functions. This architectural shift allows enterprises to compose, scale, and adapt more actively.

Meanwhile, the evolution of autonomous digital agents, driven by agentic AI, has further accelerated the need for flexibility. These agents demand adaptive and responsive systems, creating a strong push toward composable commerce models. This type of architecture enables agents to manage complex retail processes, including digital commerce, order management, supply chain operations, pricing, promotions, and last-mile delivery.

To support the practical adoption of agentic AI in retail environments, organizations need flexible, modular systems that allow digital agents to function across multiple channels. This requires a composable architecture that is headless, API-driven, and built on a microservices-based commerce platform. Without this foundation, the promise of agentic AI in omnichannel experiences cannot be fully realized.

The limitations of legacy platforms have made the shift to modern, composable systems a pressing priority for retailers. According to Precedence Research, the global composable applications market is projected to reach $7.6 billion in 2025 and is expected to increase to $31.5 billion by 2034 at a compound annual growth rate of 17.2 percent. This sharp growth reflects a broader market shift away from rigid, monolithic architectures. Transitioning to composable commerce addresses these challenges, but the journey demands more than technical proficiency. It calls for alignment with business goals, risk frameworks, and transformation road maps.

Breaking Down the Path to Modularization

With the onset of agentic AI and the drive toward autonomous systems, it is imperative to reimagine an enterprise across value streams and build an agent-first business blueprint alongside an architecture that identifies priority areas for modularization and componentization with the maximum impact on efficiency, cost, and revenue. The growing importance of social and influencer commerce further necessitates intelligent agents that can complete end-to-end transactions and ensure fulfillment.

Technology modularization begins with a structured assessment of the existing commerce stack. It involves carefully analyzing each core commerce function—customer, supplier, inventory, stock-keeping unit (SKU) and catalog, cart, checkout, promotions, order management, loyalty, and personalization. It is critical to identify areas of high friction in the order-to-fulfillment value stream and prioritize those for modularization and componentization.  

Smart agents that are based on model context protocol (MCP) for driving integration in a composable commerce ecosystem can support various areas, including conversational commerce, dark store management and fulfillment, last-mile delivery scheduling and tracking, supplier management and procurement planning, reverse logistics and returns management, and customer service and support.

In practice, retailers are witnessing strong results by decoupling customer-facing functions like digital commerce, omnichannel storefront, carts, and checkout early in the transformation process. Isolating these modules enables faster release cycles, reduces incident response time, and increases developer autonomy.

Targeted architectural changes can immediately impact revenue and customer engagement. Retailers can achieve this through the concept of dynamic, personalized bundles (smart bundles), which feature highly personalized SKUs in the bundle and contextual, targeted pricing, thereby increasing the probability of bundle adoption. These are powered by smart agents operating at the intersection of AI models and LLM-driven generative AI, delivering hyper-personalized offers at the right price point within customer conversations.

Managing Legacy Coexistence and Transformation Risk

Composable transformation is best approached through the “strangler” pattern, which involves progressively replacing legacy systems by prioritizing high-impact components that align with business goals. A modular view of the value stream helps break data silos and accelerate measurable outcomes.

Headless architecture enables omnichannel agility, while agentic AI powers intelligent interactions across the ecosystem. However, success hinges on built-in observability and recovery, ensuring performance at the component level. As legacy and modern systems coexist, managing data consistency and availability is essential for sustainable innovation.

Embedding Product Thinking, Agile Methods, and Intelligent Systems—Agentic AI-led Composable Commerce

More than modular architecture and agile execution, the full potential of composable commerce is realized through the infusion of intelligence across systems. Agentic AI, an emerging layer of AI that combines autonomy, contextual awareness, and decision-making capabilities, represents a transformative leap for retailers embracing modularity.

Unlike traditional automation, agentic AI acts proactively by surfacing insights, evaluating trade-offs, and executing autonomously. Retailers are increasingly applying agentic AI to manage promotions, bundle optimization, and demand forecasting. In recent implementations, dynamic bundling has enabled retailers to achieve 40 percent savings through AI-generated, real-time product groupings based on stock levels, purchase history, and customer intent. These bundles also contribute to faster inventory turnover and increased profitability.

Enabling Future-Ready Commerce Through Ecosystem Alliances

As composable commerce matures, its real impact lies in the architecture and the strength of the ecosystem that supports it. Scalable, intelligent systems require open standards, interoperability, and strong technology partnerships.

Strategic alliances give retailers access to pre-integrated services, reusable components, and proven frameworks—accelerating transformation while reducing complexity. In one case, such collaboration led to a fourfold improvement in time to market through optimized supply chain workflows.

This ecosystem-led model does more than improve technical efficiency. It reduces risk, prevents vendor lock-in, and creates a platform for continuous iteration. It also ensures that commerce platforms are built for today’s challenges and tomorrow’s possibilities.

Building the Future of Commerce, One Module at a Time

Transitioning from monolithic to modular commerce is not a single milestone but a strategic, iterative journey. Retailers that succeed are those willing to confront the limitations of their legacy stacks, deploy practical accelerators to scale faster and safer, manage legacy coexistence with care, build product thinking deep into their teams, and forge alliances that maximize flexibility.

There is no doubt that the future of commerce lies in systems that are modular in design, agile in operation, and intelligent at their core. Composable commerce, powered by agentic AI, offers the blueprint for that future—one module, one decision, and one customer experience at a time.

Padmanabhan Venkatesan is senior vice president and general manager of the global consumer tech vertical at Persistent Systems, leading digital product engineering for clients across retail, consumer packaged goods (CPG), travel, and logistics. With extensive experience in cloud-native platforms, microservices, and modernizing legacy technologies, he has driven digital transformation programs across industries such as communications, media, hi-tech, and retail.