Unless you’ve been living on Mars (and not yet shopping from there!), you’ve likely witnessed the transformation of eCommerce over the last few years. The shift from tightly coupled monoliths to flexible MACH architectures — Microservices, API-first, Cloud-native, and Headless — has redefined how modern digital businesses operate. These ecosystems are now composed of highly specialized systems: one handles payments, another fraud, another loyalty, logistics, customer support, and so on. Each works brilliantly within its silo.

But as AI continues to evolve, particularly in the form of autonomous agents, we are facing a new and far more complex challenge — one that goes beyond operational efficiency or automation. The idea of AI agents acting not just as assistants but as orchestrators in a MACH environment opens an entirely new dimension. And in this world, interoperability becomes more than a technical issue — it becomes the key to unlocking intelligence, synergy, and real-time, buyer-centric commerce.

I’ve been thinking a lot about this. If each MACH component is a master in its domain, how do we ensure that the value each one holds doesn’t remain trapped in isolation? What if our AI agents could learn from each of them — and then coordinate between them — in a way that elevates the entire buyer journey?

But as AI continues to evolve, particularly in the form of autonomous agents, we are facing a new and far more complex challenge — one that goes beyond operational efficiency or automation. The idea of AI agents acting not just as assistants but as orchestrators in a MACH environment opens an entirely new dimension. And in this world, interoperability becomes more than a technical issue — it becomes the key to unlocking intelligence, synergy, and real-time, buyer-centric commerce.

I’ve been thinking a lot about this. If each MACH component is a master in its domain, how do we ensure that the value each one holds doesn’t remain trapped in isolation? What if our AI agents could learn from each of them — and then coordinate between them — in a way that elevates the entire buyer journey?

When Brilliant Systems Can’t Talk

Modern MACH-based eCommerce stacks are like organizations filled with expert teams — highly skilled, deeply capable, but barely talking beyond what’s necessary. That may be enough for stable execution, but not for breakthrough performance. The buyer experience today is often a byproduct of isolated excellence, not orchestrated intention.

Despite the rise of AI in commerce, most systems are still built for deterministic automation. Agents, by contrast, bring reasoning, memory, and decision-making abilities. However, they remain confined to the scope of the individual platforms they serve. A customer service agent may be aware of a loyalty tier but may not understand the real-time logistics data that could influence refund decisions. An agent managing product recommendations may have no context on recent support tickets or pending deliveries that impact the buyer’s mood or trust.

It’s tempting to imagine solving this with a master AI — a central orchestrator that learns how to interact with each component, translates between systems, retains memory, and guides decisions. But from what I’ve seen, this can quickly become another isolated layer — powerful, yes, but potentially brittle and disconnected from the real-time pulse of commerce. And in practice, we may simply be shifting the interoperability problem one layer higher, introducing new risks of misalignment and technical debt.

What’s missing isn’t power. It’s shared understanding.

The challenge is not building smarter agents, but building agents that can collaborate meaningfully across architectural boundaries. This is particularly urgent in a MACH context, where the very appeal of modularity is undermined if intelligence cannot flow freely between modules. Without interoperability, the MACH promise of composability begins to collapse under its own weight.

A Language Before a Leader

That’s why I find the conversation around shared protocols so important. Before we build a super-agent to coordinate everything, we might first need to teach our agents how to speak to each other. Not in proprietary dialects — but through open, extensible frameworks.

This is where AGNTCY enters the discussion. Supported by Cisco, LangChain, Galileo🔭, LlamaIndex, and others, AGNTCY is one early attempt to define an agent-to-agent communication standard. It aims to do for autonomous agents what TCP/IP did for distributed computing: provide a foundational protocol layer that enables discovery, communication, and coordination across heterogeneous systems.

Harrison Chase, CEO of LangChain, believes that now that we know how to build agents, the next step is to help them connect — not in isolation, but across frameworks. That sentiment echoes across the industry.

Yash Sheth, cofounder of Galileo, has pointed out that standardization will be key to unlocking velocity in this space. Without it, each team ends up building its own infrastructure from scratch, which ultimately limits scale and collaboration. His emphasis on shared performance, accuracy, and reliability anchors the need for accountability and consistency among agents.

Vijoy Pandey, Head of Outshift by Cisco, frames this moment as a turning point — the beginning of the “Internet of Agents,” akin to the early days of the web. Just as TCP/IP and DNS brought together fragmented digital infrastructure into a cohesive system, protocols like AGNTCY could be the backbone for the next generation of collaborative AI.

We also see this narrative echoed in Forrester’s analysis. Leslie Joseph and Rowan Curran, in their March 2025 article, “Interoperability Is Key To Unlocking Agentic AI’s Future“. They highlighted the necessity of shared frameworks not only for tool integration and communication but for governance, trust, memory, and even market participation. Their insights emphasize that without a holistic view of agent cooperation, we risk building intelligent silos rather than ecosystems.

Agentic interoperability isn’t just a technical problem; it’s a conceptual one. It requires us to rethink how we model trust, identity, memory, and context across autonomous systems. It forces questions like: How does one agent verify the capabilities of another? Who governs the memory that agents use to recall past transactions? Can agents evaluate one another’s behavior over time, forming reputational insights that influence future collaborations?

And then there is the question of memory. If agents are to collaborate effectively, they must share more than APIs. They need a consistent way to retain and apply knowledge across interactions. Memory persistence, versioning, retrieval relevance — these are architectural concerns with deep implications. A shared memory layer could act as the connective tissue of the MACH ecosystem, allowing agents not only to operate but to remember and learn collectively.

In many ways, this is what would elevate eCommerce beyond its current paradigm. Not just responding to clicks and carts, but building a longitudinal understanding of the buyer, the context, and the operation behind each transaction. And doing so through distributed intelligence rather than centralized rule engines.

Preparing for the Conversations Ahead

For anyone operating in or building a MACH eCommerce stack today, this is a time to plant seeds. Encourage your technology partners to engage with interoperability efforts like AGNTCY. Begin thinking of your systems not as endpoints, but as future collaborators. Structure your data with the assumption that multiple agents — from different layers, vendors, and use cases — will one day need to understand it.

Even simple steps today — like designing clear event flows or modularizing decision-making — can position you to take advantage of what’s coming next. Preparing your architecture for machine reasoning means organizing not just your data but your intentions in ways that agents can interpret.

I believe that, over time, the orchestration layer won’t be a dashboard or control room. It will be a living network of AI agents, negotiating with one another in real-time, guided by protocols, reinforced by shared memory, and governed by clear trust boundaries. And that shift may mark the beginning of a truly intelligent commerce experience.

Because when the agents can talk, and when they can remember, reason, and act together across your architecture, the result won’t just be better operations. It will be a dramatically better buyer experience — one that feels personalized, relevant, timely, and even emotionally resonant.

Each system in your MACH ecosystem promises something great. But when they all truly talk — not just at the API level, but at the intent level — we move from technical integration to something more human: a seamless, intelligent conversation between buyer and seller.

That’s not a solved problem. But from everything I’ve seen, it’s a conversation worth starting.

And if you’re wondering whether MACH is truly ready for this next leap — it’s worth reflecting on the recent statement by the MACH Alliance President Casper Aagaard Rasmussen , who boldly stated that in his article “MACH is the backbone of an AI-ready business” This isn’t just a nod to modularity or cloud scalability. It’s a recognition that MACH architectures are fundamentally well-suited for the orchestration of AI agents — systems designed to plug in, collaborate, adapt, and evolve. With MACH as the canvas, and interoperability as the brush, agentic intelligence has a genuine shot at delivering the kind of experience eCommerce has only hinted at so far.

In the next wave of digital commerce, the winners won’t be the ones with the most tools — but the ones whose tools can think, talk, and evolve together.

—

Rafael Esberard is a seasoned Digital Transformation Executive, Consultant and Sales Leader specializing in retail and technology sectors. With a strategic, data-driven & customer-centric approach, he helps brands stay two steps ahead by analyzing emerging technology trends and translating them into high-impact sales, retail and loyalty strategies. His expertise in omnichannel integration, articial intelligence and deep market insights enable businesses to navigate complex landscapes, optimize customer engagement, and drive sustained growth. Recognized for his ability to bridge innovation with practical execution, Rafael empowers clients to anticipate market shifts and maintain a competitive edge in an ever-evolving digital economy.