Next.js + Vercel SFCC Composable Storefront

At the end of the last article I said the specs, tests, and agents that built See’s new storefront are now the same infrastructure that runs it, and that this was a story about what happens to “managed services” that deserved its own article.

This is that article.

The loop that runs the site

Here’s what post-launch support looks like on See’s today.

A delivery lead spots a problem. Or a merchandiser does. Or an automated check does. They paste it into Slack: a screen recording, an expected behavior, sometimes just a sentence. From there, an agent takes over. It triages the report against the Functional Specification Documents that are still the source of truth for the site, reproduces the behavior, and files the Jira ticket, correctly categorized, linked to the relevant spec, evidence attached. Then it writes the fix in the right repo and raises a draft PR.

The PR doesn’t arrive naked. It carries the actual test cases linked to that ticket, not a boilerplate checklist everyone ticks without reading, but the specific scenarios QA expects to pass, each one a gate before merge. When the code or the specs change, the Playwright page objects and regression suites regenerate to match. Before a release, an agent reads every ticket and PR in the version, maps the affected areas, and produces the regression plan a QA lead used to spend days assembling by hand.

And the fidelity gates from the build phase never left. Missing elements, broken assets, responsive widths, locale slips: measured checks wired into the pipeline, so a change can’t be “done” until it passes. Humans review exceptions. A named human reviews every merge. That’s the whole loop: automated QA, automated triage, ticket creation, code written right to the PR, with judgment concentrated exactly where it belongs.

Nothing in that paragraph is a roadmap. It’s running.

Why this kills the retainer

Every SI knows what a support retainer really is. Most enterprise retailers pay somewhere between $40K and $250K a month for “managed services,” and the work being done for that money is mostly staff augmentation. Tickets in, tickets out. A rotating cast of mid-level engineers keeping the lights on, and a senior architect on retainer for the weekly steering call. 64labs has always tried to stay out of this mosh pit.  We have always believed most clients could and should manage their own site. But while we hear retailers grumble about the cost of managed services, they have never seen a viable alternative beyond hiring a team for themselves and that carries its own risks. So the structure survived as a kind of IT Stockholm Syndrome. 

To be clear, this model was never about technical capability. It was about technical recruiting capability. Retailers can’t hire, manage, and retain the engineers required to run a commerce stack, so the SI fills the gap. You were never buying engineering. You were buying access to engineering. Managed services is a cleverly disguised arbitrage.

Watch the loops I described above run for a week and the math of that arbitrage collapses. The tasks that filled the staff-aug queue (bug triage, config changes, test maintenance, integration glue, analytics tagging, minor UI upgrades) are exactly the tasks agents now eat for breakfast. Scope that took eight to ten FTEs in 2024 is delivered by two or three people plus a properly orchestrated agent estate. And the smart retailer immediately asks the obvious question: if agents are doing the work, why am I paying a body-shop margin on bodies?

The billable-hours model doesn’t survive that question. Not five years from now. Now.

Managed agents: pooled, not embedded

So what replaces it? Our answer is a different unit of value entirely. Not managed services - managed agents.

Agents are not a thing you build once. Models improve, vendor stacks shift, platform APIs change. The agent you built in 2026 goes stale by 2027 unless someone maintains it, upgrades it, expands it. What a client buys in a managed-agents relationship is a guarantee: the estate of agents running their business is current, governed, and getting better, maintained by a small number of very good people who know their business and well-integrated into both client and partner technology teams.

Here’s the part that changes the economics. That estate doesn’t have to be rebuilt per client, and it doesn’t need a pod of 10 to operate. The skills our agents run on, the triage runbooks, the QA contracts, the spec-sync workflows, live in a central, shared infrastructure. Every agent session logs whether a skill worked as documented. Those logs cluster into retrospectives that open PRs against the skills themselves. A lesson learned on one client’s build ships to every client’s build. When the dust settles on launch, one person at 64labs can manage that shared infrastructure across the whole client base, making sure our best practices propagate to everyone, automatically.

Pooled infrastructure, centrally maintained, continuously self-improving. That’s why the cost reduction isn’t 20%. It’s a 50-80%. The headline fees will look small next to the old retainers. The capability behind them will be larger than anything the retainer ever bought. Your current partner is either adapting to a more demanding, lower-revenue world or going bankrupt. You should be eager to speed that outcome up one way or another.

Tough for the old boys. A leap for everyone else.

None of this is comfortable if your business depends on staff-aug volume. The firms whose commerce practices rest on post-launch managed services - and that’s most of them, giants and boutiques alike - are holding a book of business whose unit economics are about to look indefensible. The test any CIO should apply to a partner this year: are they trying to make themselves redundant, or indispensable? Tribal knowledge in their heads, agents on their infrastructure billed back to you: that’s the old model defending itself. Skills codified, agents shipped into your environment, knowledge transfer as an explicit obligation: that’s a partner you can do business with.

But for customers, this is not a story about loss. The amount of work an enterprise actually wants done is about to explode: the experimentation programs, personalization variants, taxonomy cleanups, and data passes nobody could ever justify at old prices. The cost per unit of work is collapsing, so the volume of commissioned work goes up, not down. The retailers who thrive will be the ones who either find the right managed-agents partner or are big enough to build the practice themselves: a small agent-operations team, a governed estate, and a partner measured on their team’s autonomous capability rather than monthly burn.

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64labs is a Salesforce Commerce Cloud partner and Storefront Next launch partner. If you’re paying a managed-services retainer and wondering what a managed-agents model would look like on your stack, ask us. We’ll show you the loop running on a real production site.

See's Candies is live on Storefront Next.

That makes it the first production storefront running on Salesforce's new composable framework, and the build behind it tells a more interesting story than the launch itself. 64labs started the migration before Salesforce had even finished the codebase, working alongside the platform team in real time while most partners were still waiting for GA.

The result: three production storefronts (Retail, Fundraising, and Volume Sales), over 1,800 pull requests, and a set of lessons about Storefront Next that don't exist anywhere in the documentation yet.

Three Sites

See's Candies isn't a single storefront. It runs three, each with its own checkout logic, promotional model, and operational workflow. Fundraising has a completely separate business rules engine. Volume Sales uses pricing structures that don't exist in standard B2C Commerce.

That complexity is what makes this build worth paying attention to. A single-storefront Storefront Next launch would prove the framework works. A three-storefront launch with this level of business logic proves it works at enterprise scale.

Dima Shevchuk, product owner on the project, said the multi-site architecture surfaced problems that a simpler build never would have. "Single MRT versus multiple MRT rendered some of the previously working redirects impossible," he explained. "When you have the same relative path for each site with different target URLs."

The Migration Path

Migrating from PWA Kit to Storefront Next isn't a full rewrite. The SFCC backend and API layer stay mostly the same. What changes is the frontend architecture: server-side rendering, React Server Components, a different routing model, different state management. For a team with composable SFCC experience, the learning curve is real but manageable. For a team without it, this is where projects tend to stall.

64labs had 12 PWA Kit builds in production before starting the See's migration. The code doesn't carry over one-to-one, but the architectural instincts do: where SCAPI behaves unpredictably under load, which patterns hold up long-term, and which ones become technical debt.

A significant portion of the 1,800+ pull requests went toward third-party integrations, the part of Storefront Next delivery that conference talks tend to skip over. DynamicYield for personalization, Bazaarvoice for reviews, Amplience for content management, Cybersource for payments, Experian for address validation, and OneTrust for consent. Every one of those had to be rebuilt for the new architecture.

Helen Martin, who oversaw delivery and integration strategy, said DynamicYield was manageable because the team had recently built it on PWA Kit. "We had a good line in the sand as to what we needed to build in SFN, so most of it was migratable," she said. "The challenge was the minor 20% of edge cases that only surface when you're using it in anger with a lot of UAT data coming through." The team built the integration with active consent from the start, so OneTrust was already handled before See's turned it on in production.

The Phased Rollout

See's didn't go live all at once. The team launched Fundraising first, then Volume Sales, then Retail.

Dima explained the reasoning: "FR and VS are low-traffic B2B sites, very simple, low risk. Retail is much more complex and more things can break. So we proved stability on FR and VS first."

Each storefront got its own go/no-go, its own regression pass, and its own production hardening cycle. The phased approach let the team validate the deployment pipeline, redirect strategy, and analytics parity on lower-risk sites before putting the highest-traffic storefront through a live cutover.

Where the Speed Came From

The most surprising insight from the build might be where the time savings actually came from.

When asked where the speed came from, Dima didn't point to the framework. His take was that Storefront Next itself mattered less than the AI harness and delivery workflows 64labs had built over the six months before the See's project kicked off. The tooling they walked in with on day one did more for velocity than the platform switch.

In other words, the framework is better, but the acceleration came from the operating model 64labs built around it: AI harnesses, task structures designed for LLMs, and delivery workflows that took roughly a year to develop. The framework provides the foundation. The workflow around it is what compresses the timeline.

What This Means Ahead of Dreamforce

Dreamforce 2026 kicks off September 15 in San Francisco, and Storefront Next will be one of the biggest talking points on the commerce side. Most SIs will be presenting their plans for the platform. 64labs will have already shipped it.

The See's build offers something that slides and roadmaps can't: proof that Storefront Next holds up when real enterprise complexity, real third-party integrations, and real revenue are on the line. Whether other partners can get there without the same head start is an open question, but the platform has cleared its first production test.

See's Candies is live. The framework works. And the most useful lessons from the build are the ones Salesforce couldn't have documented, because they only come from being first.

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There is a version of the AI conversation that has become painfully familiar in enterprise commerce. Teams say they are "using AI." Developers mention Claude Code. Someone demos a copy generator. A few hours get saved here and there. Meanwhile, CIOs and CTOs look at the org chart, look at the budget, and ask the only question that matters: if AI is real, why are the benefits mostly showing up as a better day for staff instead of a better business for the enterprise?

That is the right question. And it is exactly why Storefront Next on Salesforce matters.

The argument for Storefront Next is not that it gives your team a shinier storefront framework. The argument is that Salesforce is finally putting an AI-first commerce architecture on the table, one that can change how the stack is assembled, how work is divided, how much manual tuning is required, and where automation can compound over time.

For technical leaders, that distinction matters. AI that helps an individual contributor write code faster is useful. AI that changes the architecture so the enterprise can reduce complexity, automate repetitive commerce work, and create a cleaner path to measurable operating leverage is strategic.

The real problem: AI has been too personal, not operational

Most enterprise teams are seeing narrow AI gains. Developers use copilots. Analysts summarize documents faster. Merchandisers generate a few drafts instead of writing from scratch. Those are legitimate improvements, but they are local improvements. They help individuals. They do not automatically rewire the system the business runs on.

That is why so many executives are underwhelmed. If the only visible effect of AI is that smart people can do the same job a little faster, then the enterprise has not captured much value. The headcount line stays the same. Delivery models stay the same. Support models stay the same. Vendor sprawl stays the same. The organization ends up paying for AI, talking about AI, and still operating largely the same way.

That is not transformation. That is productivity theater.

Storefront Next changes the argument

Storefront Next is a full-stack React framework for B2C Commerce that combines server-side rendering with client-side navigation in a managed Salesforce architecture. Salesforce is positioning it as an AI-first storefront and has tied that positioning directly to faster implementation, AI-powered developer tooling, and simpler launch patterns.

That matters because architecture determines how far AI can go.

An inconsistent, over-customized commerce stack is hard for humans to maintain and hard for AI tools to reason about. A more opinionated, convention-driven stack is easier to scaffold, easier to extend, easier to support, and easier to automate. That means Storefront Next is not just another frontend option. It is a better substrate for AI-enabled delivery and AI-enabled operations.

This is the part technical executives should care about. The value is not only in what AI does today. The value is in choosing a platform shape that will allow more of tomorrow's build, support, merchandising, and optimization work to be delegated to AI systems with less friction.

Cimulate is the bigger story than most teams realize

Salesforce's acquisition of Cimulate is one of the clearest signals that this is not a superficial AI story. Cimulate combines real shopper behavior with simulated shopper journeys to infer intent and improve search, discovery, and recommendations in ways that go beyond traditional keyword and rules-based systems.

That should get the attention of any CIO or CTO who has watched teams spend years tuning search synonyms, patching low-conversion discovery flows, and layering manual merchandising rules on top of brittle relevance models. Cimulate's approach is built around understanding intent through both real and synthetic commerce behavior, which means the engine gets stronger not only from what happened, but from what likely could happen across millions of modeled sessions.

That is enterprise value.

Why? Because discovery is one of the most labor-intensive and under-optimized layers in commerce. Teams pour human time into relevance tuning, catalog shaping, landing page curation, campaign setup, and recommendation logic. If Salesforce can productize Cimulate inside the Storefront Next and Agentforce Commerce motion, that opens the door to automating a category of work that has historically required constant expert intervention.

That is the shift skeptical leaders should focus on. This is not AI as a sidecar. This is AI moving toward ownership of an economically meaningful layer of commerce operations.

The architecture is the real signal

Put those two moves together, the AI-first storefront and an intent-aware discovery engine, and the pattern is clear. Salesforce is not bolting AI onto the edges of commerce. It is reshaping the substrate so that more of the work can eventually be automated rather than hand-tuned.

For technical leaders, that reframes the evaluation. The question is no longer whether AI is useful. It is whether your architecture is built to let AI compound. Choosing a platform shape that AI systems can reason about, extend, and operate is the first real decision, and it is the one that determines how much value everything after it can capture.