Velocity Ascent Live

Fashion Tech, Revisited: From Sketch-to-Storefront to Storefront-to-Agent

Velocity Ascent Live · June 1, 2026 ·

The future I sketched in 2024 is no longer hypothetical. It’s shipping, it’s backfiring, and it’s rewriting who wins.

A follow-up to “Fashion Tech: The Present Future of Fashion and Technology” (October 2024)

When I published that first piece in October 2024, I argued that the blend of fashion and artificial intelligence was creating “limitless possibilities.” Roughly eighteen months on, the possibilities are no longer hypothetical. They are shipping. They are also forcing the harder questions the original post only gestured at: not just whether we can generate a model, a garment, or a storefront, but whether we should, and on whose terms.

The original throughline still holds. I compared today’s AI tooling to the sewing machine of the 19th century, a technology that reshaped how fashion is made, marketed, and consumed, mostly by collapsing time and cost. That frame has aged well. What I underestimated was the speed of the collapse, and how quickly the conversation would shift from efficiency to ethics, labor, and trust.

Here is where things actually landed.

The trajectory I called: hyper-personalization went from edge to baseline

In 2024 I described hyper-personalization, virtual fitting rooms, and digital “Sketch-to-Storefront” workflows as the leading edge. They are now closer to table stakes. By early 2026, McKinsey’s fashion technology outlook put adoption of machine learning for trend forecasting, planning, and 3D sample generation at roughly 48% of global brands. The AI-generated fashion photography market, barely a line item when I was writing, grew from about $1.51 billion in 2024 to around $2.01 billion in 2025.

The design tools I singled out, Browzwear and Lalaland.ai, did not just survive; they matured into a connected pipeline. Browzwear’s framing for 2026 is “idea to twin to shelf,” with the same digital twin now feeding not only fit and production but AI-generated marketing imagery. Virtual sampling, by various industry estimates, now cuts sample-development cost by 60 to 70% and time-to-market by up to half. The “first physical sample is the only sample” pitch I quoted in the original has, for a lot of teams, simply become true.

The proof point is no longer a vendor demo. MAS Holdings, the apparel manufacturer behind many of the intimates, swimwear, and performance brands you already know, (Victoria’s Secret, Nike, lululemon) began its digital product creation push in 2017 and stood up a dedicated Centre of Excellence in 2020.

It now develops more than 4,000 unique 3D styles a year for over 50 brands, with real-time co-creation replacing rounds of physical samples. The faster lead times and lower fabric waste arrived as byproducts of integration, not as one-off stunts. That is the version of “Sketch-to-Storefront” I was describing in 2024, finally operating at industrial scale.

Shift one: the synthetic model walked out of the back office and onto the cover

In the first piece I treated AI imagery mostly as a production convenience. The technology has since become good enough to be indistinguishable from a photograph, and that is exactly where the trouble started.

Mango ran AI-generated models in a 2024 campaign, with its CEO defending the move on speed grounds. Levi’s tested AI models and then walked the messaging back, insisting it was not a diversity strategy and that live shoots would continue.

Then, in August 2025, a Guess advertisement in Vogue, produced by the AI studio Seraphinne Vallora, became a genuine firestorm. The images were polished enough to read as editorial photography; only a small disclaimer revealed the model did not exist. The backlash was loud, and it was not really about image quality. It was about disclosure, displaced creative labor, and the narrow, conventionally “perfect” definition of beauty that synthetic models tend to default toward.

Regulation moved in parallel. The EU AI Act, adopted in 2024 and rolling out through 2027, pushes toward disclosure of synthetic media, transparency around training data, and respect for intellectual property, even when the underlying tools are open-source.

This is the part of the landscape closest to my own work, so I will be direct about the lesson. The defensible position is not “we used AI” versus “we didn’t.” It is whether you can show your work: where the training imagery came from, whether it was used with consent or under a clean license, and whether the audience was told.

Provenance stopped being a compliance footnote and became part of the product. The brands that weather the next backlash will be the ones that can answer “where did this image come from?” without flinching.

Shift two: the storefront itself is starting to dissolve

My 2024 piece assumed a shopper who visits a storefront, even a hyper-personalized one. The most disorienting development since then is that the visit may not happen at all. The shopper increasingly delegates to an agent.

The infrastructure arrived fast. OpenAI introduced an Agentic Commerce Protocol with Stripe; Google launched a Universal Commerce Protocol at NRF in January 2026; Microsoft shipped Copilot Checkout; Shopify rolled out agentic storefronts that let merchants sell inside ChatGPT, Copilot, and Gemini at once. Walmart and OpenAI announced a buy-in-chat partnership in October 2025, and Amazon extended its “Buy for me” capability while pointing its Rufus assistant toward comparison and autonomous purchasing. Shopify has said orders originating from AI-powered search grew roughly fifteen fold year over year.

And yet the reality check is just as instructive. OpenAI quietly paused its Instant Checkout, with fewer than 30 of Shopify’s millions of merchants live, conceding the experience lacked the flexibility it wanted. Walmart reportedly saw in-chat purchase conversion run about three times lower than sending shoppers to its own site. Forrester’s read on the moment was blunt: everyone has the fear of missing out, and nobody has actually figured it out yet.

For anyone building in this space, and I spend most of my week here, the takeaway is unglamorous but clear. The near-term winners are not the brands automating checkout most aggressively. They are the ones making their catalogs, inventory, pricing, and trust signals as readable by machines as by humans. The storefront is becoming an API. Fashion brands that still treat product data as marketing copy, rather than structured source-of-truth data, will simply be invisible when an agent comes shopping.

The map redrew itself, and not only because of technology

My original piece ranked the top online fashion retailers by 2023 sales: Shein at $14.4 billion, Walmart at $12.3 billion, Amazon at $8.4 billion. That snapshot is already a historical document, and the force that dated it was policy, not algorithms.

In 2025 the U.S. ended the “de minimis” exemption that had let sub-$800 parcels enter the country duty-free, the loophole that made the Shein and Temu model viable at scale. Prices rose, both companies warned customers directly, and transactional data showed price-sensitive shoppers migrating toward off-price department stores, secondhand apps, and domestic players. It is a useful corrective to any tech-first narrative: the consumer’s behavior is shaped at least as much by tariff schedules and trade policy as by virtual try-on.

The story so far is mostly digital, but one of the more interesting sustainability bets of 2026 is chemical.

In April, the Bezos Earth Fund committed $34 million to rethink what clothes are actually made of, on the logic that materials and manufacturing account for roughly 80% of fashion’s environmental footprint. The largest grant, $11.5 million, went to Columbia University in partnership with the Fashion Institute of Technology to grow a textile fiber from bacteria fed on agricultural waste: strong and breathable, compostable at end of life, requiring almost no land, and producing no microplastic pollution.

A fiber whose origin is a documented feedstock and a known biological process is provenance pushed all the way down to the molecule. The question the industry asks: “where did this come from?”, is now being asked of the thread itself.

The real divider isn’t AI adoption. It’s digital maturity.

Step back from any single tool and a pattern emerges. The brands getting caught flat-footed and the ones quietly compounding advantage are not separated by whether they use AI. They are separated by how deeply it is woven in.

A useful framing here comes from Browzwear’s digital-maturity work: maturity is not the act of adopting tools, it is integrating them into every part of the business until they drive continuous, scalable impact. Buying the software is the easy part. Rewiring how the organization works around it is the actual transformation.

That work sketches a five-phase climb, and it maps almost too neatly onto the stories above:

The analog trap. Traditional, siloed processes, no real strategy, digital as an afterthought.
Reactive experimentation. Isolated pilots run by individual teams, no executive sponsorship, wins that never scale past the team that ran them.
Intentional progress. A genuine digital strategy appears, often anchored by a dedicated Centre of Excellence, but adoption is still patchy across functions.
Integrated growth. Leadership-backed, cross-departmental, with digital embedded in day-to-day operations rather than bolted on.
Digital maturity. Agile, data-driven, customer-centric. Digital is no longer an add-on; it is the core the business runs on.

Seen through that ladder, a lot of last year’s headlines look less like innovation and more like Phase 2 wearing a Phase 5 costume. A splashy AI ad with a near-invisible disclaimer is an isolated pilot optimized for a press cycle, not an integrated practice with disclosure and provenance designed in from the start. The paused in-chat checkout was the same reflex at platform scale: ship the demo, skip the plumbing. Real maturity is comparatively boring. It looks like a manufacturer quietly producing thousands of digital styles a year because the whole organization, not one enthusiastic team, was rebuilt around it.

Provenance-clean, properly licensed training data is what lets you use AI imagery without inviting a backlash.

Here is where it connects back to my own throughline, and why I do not treat the ethics question and the agentic-commerce question as separate from this. The discipline that gets you up the maturity ladder, clean integrated data and a single source of truth, is the same discipline that makes you ready for both shifts.

Provenance-clean, properly licensed training data is what lets you use AI imagery without inviting a backlash. Structured, machine-readable product data is what lets an agent actually find and buy what you sell. Disclosure, provenance, and machine-readability are not three separate compliance chores. They are three faces of one mature operation that knows where its data comes from and where it goes.

NY Tech Week event at The Fashion Institute of Technology.

Field notes from FIT Tech Week: the bleeding edge

I recently attended the Fashion Institute of Technology’s Tech Week event, listening to the founders actually building this layer, and the view from inside is more concrete, and more interesting, than the trend pieces suggest.

The sharpest demonstration came from Emily of Make the Dot, who walked through producing a denim collection called OSSA in roughly the time it usually takes to schedule a fitting. The old playbook she is replacing is familiar: watch what is selling, copy the winning trends, order in enormous quantities, and eat the waste. Her version inverts it.

An agent, “Dot,” aggregated the entire signal chain, from runway to brands to influencers to social to Google search trends, and generated design variations out of the pattern in that data. A human, Nicole, curated, deciding which pieces actually made the collection. Fit was tested digitally, and the agent produced a line sheet specified down to the wash, the whiskers, and the PP spray. Because the supply chain is vertically integrated, cotton to mill to cut-and-sew, a cycle she described as “six plus six” now runs in about four weeks.

Make the Dot is an AI-native product development platform that connects design, development, and merchandising in a single digital workflow.

Two of her lines stuck with me. “When making something small is nimble, a bet becomes a test,” she said, describing how a brand can float a social ad to gauge demand before committing a single yard to production. And the one that quietly reframes the whole sustainability conversation: “the path to the least waste also means more time for creativity.” She delivered it wearing the jeans.

That is the optimistic, operational story. The panel I sat in on, “Beyond the Headlines: The Real Ways AI Is Changing Fashion,” moderated by Rachel Sterling of The Pattern Maker and Alternew, pushed on where this goes next, and the founders mostly agreed the change is structural rather than cosmetic.

Franz Tschimben of ALLSIDES was candid that the specifics are hard to predict, but bet that 3D will emerge as a default mode of content creation as the cost of producing it collapses, the same digital-twin logic from earlier in this piece, generalized. Yusan Lin of Mirror Mirror AI described a decentralization of discovery: a world where anyone can be scouted, rather than waiting to be found by a gatekeeper. Sreya Halder of The Mall extended that to creation itself, a world where everyone gets to build their own village and curate it themselves.

Then Sophia Sterling, formerly of Google Creative Lab and now building a stealth venture called Paprika, planted the flag I found most worth carrying home. The company is named for the Diana Vreeland line, “a little bad taste is like a nice splash of paprika,” her argument against the tyranny of no taste at all. Sterling’s pitch is for what lives outside the algorithm: the human, physical, slightly-wrong instinct that recommendation engines flatten on contact.

It is the same tension the rest of this piece keeps circling. Dot can aggregate every signal in the market, but Nicole still decides what is good. The tools democratize who gets to make and be seen, and in the same breath they raise the value of the one thing they cannot generate, a point of view. If the last eighteen months were about proving the machinery works, the bet these founders are placing is that the next eighteen are about taste.

What this means for “the present future”

The sewing machine analogy I leaned on still works, but it needs a second half. The sewing machine made garments faster and cheaper, and in doing so it created entirely new questions about labor, standardization, and who got to call themselves a maker. AI is repeating that pattern at compressed speed across the whole pipeline, from concept render to synthetic model to autonomous checkout. The efficiency is real. So are the questions.

If the 2024 story was “limitless possibilities,” the 2026 story is that the possibilities now carry a price of admission, and that price is maturity: disclosed AI, consented and licensed training data, traceable provenance, and honest, machine-readable product information, all integrated rather than bolted on. The brands and builders who treat that as a constraint will keep getting caught flat-footed. The ones who treat it as the design spec are, I think, the ones who actually inherit the present future.

Joe Skopek is the founder of Velocity Ascent, an AI-first innovation consultancy based in New York and a member of the Leadership Team of the NYC Chapter of the NANDA Project from MIT Media Lab.

Sources and further reading

McKinsey & Company, The State of Fashion 2026: When the rules change (Nov 2025); McKinsey fashion technology outlook on ML adoption (early 2026)

Business of Fashion, on generative AI and virtual try-on (Jan 2026)

FASHN AI, Fashion AI: 7 Key Use Cases in 2026 (AI-generated photography market sizing, Feb 2026)

Browzwear, The Future of Digital Product Development: Trends Shaping Fashion in 2026 (“idea to twin to shelf”); Browzwear, A Digital Maturity Framework for Brands & Manufacturers (five-phase maturity model, MAS Holdings case study)

CNN, AI models in Vogue (Mango and Levi’s context, Jul 2025); Good Morning America / FashionNetwork, on the Guess-Vogue / Seraphinne Vallora campaign (Aug 2025)

Fast Company, Shop ’til you bot (agentic commerce, Instant Checkout pause, 2026); commercetools, The Agentic Commerce Radar (protocols, Walmart conversion data, 2026); Digital Commerce 360 (platform strategies, Apr 2026)

WWD and Morning Consult, on the end of de minimis and the Shein/Temu impact (2025); Business of Fashion, Will the End of De Minimis Kill the Shein Haul? (Jul 2025)

Bezos Earth Fund, Reinventing Clothes: $34 Million in Grants (Apr 2026); Columbia Engineering, on the $11.5M Columbia/FIT bacterial-fiber grant (Apr 2026)

FIT Tech Week (2026), author’s field notes: panel “Beyond the Headlines: The Real Ways AI Is Changing Fashion” (moderator Rachel Sterling, The Pattern Maker / Alternew; panelists Franz Tschimben, ALLSIDES; Yusan Lin, Mirror Mirror AI; Sreya Halder, The Mall; Sophia Sterling, Paprika), and the Make the Dot presentation (Emilie Ho, co-founder and CEO). Vreeland quote from D.V. (1984)

The Missing Middle: How IoT and Agentic AI Are Converging on the Same Infrastructure

Velocity Ascent Live · May 14, 2026 ·

What the shift from fixed hardware to portable intelligence means for your organization

Most discussions around AI focus on models. Most discussions around IoT focus on devices. Both miss the more consequential shift happening beneath both industries: the decoupling of supervisory software from proprietary hardware ecosystems.

Across industrial automation, edge computing, and distributed AI systems, organizations are deploying vendor-neutral orchestration layers that coordinate workloads across heterogeneous infrastructure — not because the hardware changed, but because the coordination logic no longer has to be bound to it. The result is software-defined operational infrastructure: architectures where deployment logic, analytics, and control are more portable than the physical systems they govern.

This shift is most visible where distributed autonomy must operate under real-world constraints — cybersecurity requirements, regulatory frameworks, interoperability limits, latency boundaries, and the physical realities of industrial environments. These aren’t edge cases. They are the environment.

The real infrastructure shift isn’t in the hardware, it’s in the coordination layer above it. When supervisory software decouples from proprietary ecosystems, the entire operational stack becomes composable.”

Simultaneously, AI research is moving beyond isolated models toward networked ecosystems of cooperating agents. Rather than a single model responding to prompts, these architectures involve distributed populations of specialized agents capable of delegation, negotiation, memory exchange, tool use, and coordinated execution across cloud and edge environments. The MIT Media Lab’s NANDA framework sits at the intersection of this work – treating agents not as assistants but as interoperable operational services that can discover, coordinate with, and supervise other agents across distributed infrastructure layers. The framework can be defined as an “Internet of AI Agents” -this is more than a metaphor. It describes a topology.

This is the moment of convergence for IoT. Traditional architectures depend on tightly coupled device logic, vendor-specific integrations, and centralized supervisory platforms. Agent-oriented orchestration replaces that rigidity with adaptive supervisory layers that coordinate heterogeneous devices, data streams, control systems, and edge workloads through higher-level semantic and operational abstractions. The hardware doesn’t have to change. The coordination model does.

The Shift From Fixed Hardware to Portable Intelligence

For decades, industrial and operational systems were built around tightly integrated hardware stacks. PLCs (Programmable Logic Controllers), SCADA (Supervisory Control and Data Acquisition) systems, embedded controllers, and industrial gateways were often deeply tied to specific vendors and deployment models. Expanding or modernizing those environments typically required significant infrastructure replacement and operational disruption.

That model is evolving.

Modern edge platforms are moving toward software-defined operations where orchestration, supervision, and intelligence exist independently from the underlying hardware layer. Applications are increasingly containerized. Workloads are portable. Infrastructure is abstracted. Supervision is decoupled from hardware.

This creates operational flexibility that most older architectures were never designed to support.

An intelligent workload that once depended on a specific physical appliance can now move between hardware environments with minimal reconfiguration. AI inference can execute locally at the edge rather than relying exclusively on centralized cloud infrastructure. Operational systems can scale horizontally across fleets of devices rather than vertically through increasingly expensive proprietary infrastructure.

In industrial environments, this transition is already being operationalized through virtual PLCs, edge-native SCADA, and software-defined automation frameworks. In AI infrastructure, the same architectural logic is driving the shift toward distributed agentic systems — where intelligence, like workloads, is becoming something that can be coordinated across environments rather than anchored to any single one.s virtual PLCs, edge-native SCADA, and software-defined automation.

The Convergence Between Industrial Edge and Agentic AI

Industrial automation and agentic AI appear to belong to separate categories. They are beginning to solve the same problems.

Modern agentic systems operate as distributed execution environments, not standalone applications. Multiple agents coordinate asynchronously across systems and contexts. Some workloads execute locally. Others route through centralized orchestration. Human approval gates, telemetry, policy enforcement, audit trails, and workload supervision are operational necessities – not optional features.

This is industrial infrastructure logic applied to AI.

The challenge is no longer generating outputs from a model. It’s supervising a distributed network of intelligent processes across heterogeneous environments while maintaining reliability, governance, and operational traceability. That is precisely the problem edge orchestration platforms were built to address – managing software updates, security policies, telemetry, workload deployment, fault monitoring, and rollback capability across thousands of distributed nodes, regardless of underlying hardware vendor or device architecture.

Agentic AI systems are encountering the same operational realities. The nodes are no longer just physical machines – they are inference workloads, autonomous agents, localized automation systems, and policy-bound execution environments.

Industrial edge infrastructure is becoming software-native. Agentic AI is becoming infrastructure-native. The gap between them is closing faster than either community has recognized.

Why Hardware Abstraction Matters to the C-Suite

One of the largest operational challenges in both IoT and distributed AI is fragmentation — and it’s almost never a deliberate choice. Infrastructure accumulates over time. Vendors change. Acquisitions happen. Deployment environments diverge. The result is operational complexity that compounds as organizations scale, and that complexity has a cost: slower deployment cycles, higher maintenance overhead, and technology lock-in that constrains future investment decisions.

Hardware abstraction addresses this at the architectural level. Rather than building operational logic around specific hardware platforms, organizations manage workloads through software layers capable of deploying and supervising across heterogeneous device environments simultaneously. The infrastructure beneath becomes largely irrelevant to the operational layer above it.

For executive decision-makers, this translates into four concrete advantages.

Capital flexibility. Hardware strategies can evolve without rewriting operational systems – reducing the switching costs that typically lock organizations into legacy vendor relationships.

Deployment speed. New edge infrastructure can be provisioned remotely through zero-touch deployment models, compressing rollout timelines and reducing dependence on field engineering resources.

Operational resilience. Workloads are no longer anchored to specific hardware. When devices fail or conditions change, execution shifts – automatically and without manual intervention.

AI at operational scale. Inference can run locally where latency, bandwidth, privacy, or regulatory constraints make centralized cloud execution impractical – unlocking AI deployment in environments where it previously couldn’t operate.

These are not just IT considerations that surface in infrastructure reviews. They are strategic constraints on how quickly organizations can move, how much they pay to maintain what they’ve built, and how much leverage they carry into vendor negotiations.

The Companies Driving the Shift

The market for edge orchestration and distributed operational infrastructure is still forming, and the vendors shaping it are approaching it from distinctly different angles — reflecting the diversity of the environments they were built to serve.

Companies such as ZEDEDA and SUSE Industrial Edge anchor the enterprise end of the market, with platforms built around Kubernetes-native deployment, large-scale fleet supervision, and hardware-agnostic lifecycle management. Their architectures are designed for organizations operating thousands of distributed nodes across complex, multi-vendor environments.

A different set of vendors — including Barbara and Mutexer — are focused on industrial modernization from the operational technology side. Their work centers on OT/IT convergence, software-defined automation, and reducing dependency on tightly coupled legacy hardware. Where the first group abstracts infrastructure for cloud-native operators, this group is meeting industrial environments where they actually are.

Platforms such as Clea by SECO and FairCom Edge occupy a more embedded tier — emphasizing telemetry, OTA lifecycle management, and lightweight edge AI deployment for constrained hardware environments where full Kubernetes orchestration isn’t practical.

Open-source ecosystems are also emerging as a significant force. Projects including KubeEdge, Open Horizon, and EdgeX Foundry are increasingly attractive to organizations prioritizing vendor neutrality, sovereign infrastructure control, or air-gapped deployments — particularly in regulated industries and public sector contexts.

Taken together, these efforts reflect a market converging on a shared architectural premise: that operational intelligence should be portable, distributed, and decoupled from the hardware layer beneath it. The vendors disagree on implementation. They agree on direction.

Leading vendors: hardware-agnostic edge control, orchestration, and supervision software

A few companies consistently emerge as leaders in this space — particularly across industrial automation, IIoT, edge AI, and distributed operations.

Here are some of the current strongest players by category as defined in our research:

Provider	Core Focus	Strengths	Typical Customers
ZEDEDA	Edge orchestration & lifecycle management	Strong hardware abstraction, zero-touch deployment, Kubernetes/VM support	Industrial, retail, telecom, energy
Barbara	Industrial edge AI platform	OT/IT convergence, container orchestration, broad protocol support	Utilities, manufacturing, energy
SUSE Industrial Edge	Industrial edge infrastructure	Kubernetes-native, GitOps workflows, scalable fleet ops	Enterprise industrial operations
Clea by SECO	Full-stack edge/IoT framework	Hardware-agnostic orchestration, OTA, AI deployment	OEMs, embedded systems vendors
Eclipse ioFog	Open-source EdgeOps	Distributed workload orchestration, air-gapped deployments	Defense, industrial, research
FLECS	Industrial software layer	Software-defined automation environments	Machine builders, automation OEMs
FairCom Edge	Industrial data integration	OT protocol translation, edge persistence, telemetry	Manufacturing, utilities
Mutexer	Virtual PLC / SCADA platform	Software-defined controls on generic Linux hardware	Modern industrial automation teams

How the market is taking shape

The market is converging around Kubernetes-native, containerized edge orchestration — and “hardware-agnostic” has become a term of art with fairly consistent meaning across vendors: ARM and x86 compatibility, support for hardware platforms such as NVIDIA Jetson, Intel, and industrial IPCs, container portability across virtualized environments, and independence from proprietary PLC ecosystems.

The clearest differentiator between platforms is where they sit in the stack. Some — including ZEDEDA and SUSE — focus on IT-style edge orchestration: abstracting heterogeneous hardware and managing large-scale distributed infrastructure. Others, such as Barbara and Mutexer, target industrial OT environments directly, working to replace tightly coupled PLC and SCADA stacks with portable software layers. A third group — including Clea by SECO and FairCom Edge — centers on IoT telemetry, OTA lifecycle management, and lightweight edge AI deployment.

For industrial control specifically, the most consequential trend is the move toward virtual PLCs, software-defined automation, edge-native SCADA, and AI-assisted operations at the edge. This is why vendors like Mutexer and Barbara are drawing attention: they’re not just modernizing the interface to industrial systems — they’re attempting to replace the underlying control architecture entirely.

ZEDEDA occupies a different position: a recognized horizontal platform that abstracts heterogeneous edge hardware and supports distributed management at scale, without being tied to any specific industrial vertical.)

The open-source layer

Open-source ecosystems are playing an increasingly significant role. Projects including Eclipse ioFog, KubeEdge, Open Horizon, and EdgeX Foundry tend to surface in environments where vendor neutrality is a priority, air-gapped deployments are required, or organizations need to avoid cloud lock-in — conditions common in regulated industries, defense-adjacent infrastructure, and public sector deployments.

One way to read the competitive landscape:

Category	Representative vendors
Cloud-native edge infrastructure	ZEDEDA, SUSE, ioFog
Industrial automation modernization	Barbara, FLECS, Mutexer
Embedded / IoT edge platforms	Clea, FairCom
AI-centric edge orchestration	Barbara, Clea, TwinEdge

The convergence that matters

What makes this moment distinct is that the convergence isn’t just between IT and OT – it’s between physical operational infrastructure and the emerging layer of distributed agentic AI. The platforms being built today to supervise heterogeneous edge hardware are, in many respects, the same platforms that will eventually coordinate heterogeneous agent ecosystems. The infrastructure problem and the AI problem are becoming the same problem.

Velocity Ascent helps organizations think through the infrastructure and AI questions that don’t have obvious answers yet. If that’s where you are, we should talk.

Learn More: Core Concepts — A Plain-English Overview

What Are Industrial Edge Systems?

Industrial edge systems are localized computing environments that sit close to physical operations rather than inside centralized cloud infrastructure.

Rather than routing every sensor reading, command, or signal back to a distant data center, edge systems process information at or near its source. The practical effect is meaningful: lower latency, stronger resilience, and the ability to keep operations running even when cloud connectivity is interrupted or unavailable.

Examples include factory floor automation, utility monitoring infrastructure, logistics and warehouse operations, transportation systems, energy infrastructure, and oil and gas facilities.

These environments typically operate continuously, under strict requirements for reliability, low latency, and operational oversight — conditions that make edge processing not just useful, but necessary.

What Are Agentic AI Systems?

Agentic AI systems are environments where software agents perform tasks autonomously or semi-autonomously on behalf of users or organizations.

Unlike a traditional chatbot that generates a single response, an agentic system can retrieve information, make decisions, coordinate with other agents, trigger workflows, monitor systems, generate outputs, request approvals, and execute operational tasks — often in sequence, and often without direct human involvement at each step.

A mature agentic system behaves less like a standalone application and more like a distributed operational workforce: specialized digital actors operating under defined rules, permissions, and supervisory controls.

What is IoT?

IoT “the Internet of Things” refers to the connection of physical devices to digital networks so they can collect, transmit, receive, and act on data.

The devices themselves span a wide range: environmental sensors, smart meters, connected industrial machinery, surveillance systems, wearables, fleet tracking hardware, and building automation systems, among others.

The core idea is straightforward but consequential: physical infrastructure becomes digitally observable and, increasingly, digitally controllable.

What Is Hardware-Agnostic Edge Control Software?

Hardware-agnostic edge control software is a supervisory layer that manages distributed systems regardless of who manufactured the underlying hardware.

Traditionally, operational systems were tightly bound to proprietary hardware ecosystems with software and hardware sold and maintained together, by the same vendor, on the same roadmap.

Modern orchestration platforms break that coupling. Workloads become portable. Hardware becomes interchangeable. Vendor lock-in becomes a choice rather than a structural constraint.

In practice, this means organizations can deploy workloads across mixed hardware fleets, centrally supervise distributed systems, push software updates remotely, scale without replacing infrastructure, standardize governance and security policies, and run AI workloads across diverse environments – all through a single coordination layer.

In simple terms: software intelligence becomes more portable than the hardware beneath it.

What Are PLCs and SCADA Systems?

PLCs and SCADA systems are two foundational technologies in industrial operations – and increasingly, two of the most important targets for modernization.

A PLC (Programmable Logic Controller) is a rugged industrial computer designed to control machinery and operational processes in environments such as factories, utilities, and infrastructure facilities. A SCADA (Supervisory Control and Data Acquisition) system sits above that layer, providing centralized visibility across an industrial environment: collecting telemetry, displaying operational status, triggering alerts, and allowing operators to monitor or intervene across distributed systems.

Historically, both were highly proprietary – hardware and software sold together, deeply coupled to specific vendor ecosystems, and difficult to update or extend without significant infrastructure investment.

Modern edge orchestration platforms are beginning to change that, virtualizing and modernizing these environments through software-defined approaches that decouple operational logic from the hardware beneath it.

What Is NANDA?

NANDA – the Networked Agents and Decentralized Architecture framework, developed at the MIT Media Lab – is an emerging standard for how AI agents discover, communicate with, and coordinate across distributed infrastructure.

Where most AI frameworks focus on what a single model can do, NANDA focuses on what networks of agents can do together. Agents in a NANDA-aligned system are designed to be interoperable, capable of finding other agents, negotiating tasks, delegating work, and operating across mixed infrastructure without requiring a centralized controller to manage every interaction.

The framework is sometimes described conceptually as an “Internet of AI Agents” – a topology where agents function less like applications and more like addressable services that can be composed, coordinated, and supervised at scale.

For organizations thinking about edge infrastructure and distributed operations, NANDA is worth watching. The same architectural problems it addresses in AI – heterogeneous environments, decentralized coordination, autonomous execution under governance constraints – are the problems industrial edge platforms have been working on for years. The two bodies of work are converging on the same solution space.

Scaling Digital Production Pipelines

Velocity Ascent Live · February 11, 2026 ·

Agentic Infrastructure in Practice

Enterprise AI conversations still over-index on models, focusing on benchmarks, parameter counts, feature comparisons, and release cycles. Yet production environments rarely fail because a model lacks capability. They often fail because workflow architecture was never designed to absorb autonomy in the first place.

When digital production scales without structural discipline, governance erodes quietly. When governance tightens reactively, innovation stalls. Both outcomes stem from the same architectural flaw: layering AI onto systems that were not built for persistent context, background execution, and policy-bound automation.

The competitive advantage is not in the model – it is in the pipeline.

The institutions that succeed will not be those experimenting most aggressively. They will be those that design structured agentic systems capable of increasing throughput while preserving accountability. In that environment, the competitive advantage is not the model itself but the production pipeline that governs how intelligence moves through the organization.

The question is not whether to use AI. The question is whether your infrastructure is designed for autonomy under constraint.

Metaphor: The Factory Floor, Modernized.

Think of a legacy archive or production system as a dormant factory. The machinery exists. The materials are valuable. The workforce understands the craft. But everything runs manually, station by station. Modernization does not mean replacing the factory. It means upgrading the control system.

CASE STUDY: Sand Soft Digital Arching at Scale
In the SandSoft case study, the transformation began with physical ingestion and structured digitization. Assets were scanned, tagged, layered into archival and working formats, and indexed with AI-assisted metadata.

That was not digitization for convenience. It was input normalization. Once the inputs were stable, LoRA-based model adaptation was introduced. Lightweight, domain-specific training anchored entirely in owned source material .

Then came the critical layer: agentic governance.

Watermarking at creation. Embedded licensing metadata. Monitoring agents scanning for IP misuse. Automated compliance reporting. This is not AI as a creative distraction. It is AI as a controlled production subsystem.

Each agent has a bounded mandate. No single node controls the entire flow. Every output is logged. Escalation paths are predefined. Like a well-run enterprise desk, authority is layered. Execution is distributed. Accountability remains human.

That is the difference between experimentation and infrastructure.

Why This Matters to Senior Leadership

For CIOs, operating partners, and infrastructure decision-makers, the core risk is not technical failure but unmanaged velocity. Agentic systems accelerate output, and if governance architecture does not scale in parallel, exposure compounds quietly and often invisibly.

A disciplined production pipeline does three things:

Reduces manual drag without decentralizing control
Creates persistent institutional memory through logged workflows
Converts AI from cost center experiment to auditable operational asset

In regulated or credibility-driven environments, autonomy without traceability creates risk. When agentic systems are deliberately structured, staged in maturity, and governed by explicit policy constraints, they shift from liability to resilience infrastructure. The distinction is not cosmetic. It is structural. This is not about layering AI tools onto existing workflows. It is about redesigning how work moves through the institution – with autonomy embedded inside accountability rather than operating outside it.

For leaders responsible for credibility, the most significant risk of agentic AI is not technical failure per se but unmanaged success – systems that move faster than oversight can absorb can create risk exposure that quietly accumulates. A recent McKinsey analysis on agentic AI warns that AI initiatives can proliferate rapidly without adequate governance structures, making it difficult to manage risk unless oversight frameworks are deliberately redesigned for autonomous systems. Similarly, enterprise practitioners have cautioned that rapid deployment without structural guardrails can create a shadow governance problem, where velocity outpaces policy enforcement and exposure compounds before leadership has visibility.

Agentic systems do not create exposure through failure. They create exposure when success outpaces oversight.

The opportunity, however, is substantial. Well-designed agentic workflows reduce manual drag, surface meaningful signal earlier in the lifecycle, and preserve human judgment for decisions that matter most. By embedding traceability, auditability, and policy enforcement directly into operational workflows, organizations create durable institutional assets – documented reasoning, consistent standards, and reusable analysis that withstand turnover and regulatory scrutiny.

This is how legacy organizations scale responsibly without eroding trust or sacrificing control.

Elevator Pitch

We are not automating judgment. We are structuring production pipelines where agents ingest, analyze, monitor, and validate under explicit policy constraints, while humans remain accountable for consequential decisions. The objective is scalable output with embedded governance, not speed for its own sake.

Less Theory, More Practice: Agentic AI in Legacy Organizations

Velocity Ascent Live · December 22, 2025 ·

How disciplined adoption, ethical guardrails, and human accountability turn agentic systems into usable tools

Agentic AI does not fail in legacy organizations because the technology is immature. It fails when theory outruns practice. Large, credibility-driven institutions do not need sweeping reinvention or speculative autonomy. They need systems that fit into existing workflows, respect established governance, and improve decision-making without weakening accountability. The real work is not imagining what agents might do in the future, but proving what they can reliably do today – under constraint, under review, and under human ownership.

From Manual to Agentic: The New Protocols of Knowledge Work

Most legacy organizations already operate with deeply evolved protocols for managing risk. Research, analysis, review, and publication are intentionally separated. Authority is layered. Accountability is explicit. These structures exist because the cost of error is real.

Agentic AI introduces continuity across these steps. Context persists. Intent carries forward. Decisions can be staged rather than re-initiated. This continuity is powerful, but only when paired with restraint.

In practice, adoption follows a progression:

Manual – Human-led execution with discrete software tools
Assistive – Agents surface signals, summaries, and anomalies
Supervised – Agents execute bounded tasks with explicit review
Conditional autonomy – Agents act independently within strict policy and audit constraints

Legacy organizations that succeed treat these stages as earned, not assumed. Capability expands only when trust has already been established.

Metaphor: The Enterprise Desk

How Agentic Roles Interact

A useful way to understand agentic systems is to compare them to a well-run enterprise desk.

Information is gathered, not assumed. Analysis is performed, not published. Risk is evaluated, not ignored. Final decisions are made by accountable humans who understand the consequences.

An agentic pipeline mirrors this structure. Each agent has a narrow mandate. No agent controls the full flow. Authority is distributed, logged, and reversible. Outputs emerge from interaction rather than a single opaque decision point.

This alignment is not cosmetic. It is what allows agentic systems to be introduced without breaking institutional muscle memory.

Visual Media: Where Restraint Becomes Non-Negotiable

Textual workflows benefit from established norms of review and correction. Visual media does not. Images and video carry implied authority, even when labeled. Errors propagate faster and linger longer.

For this reason, ethical image and video generation cannot be treated as a creative convenience. It must be governed as a controlled capability. Generation should be conditional. Provenance must be explicit. Review must be unavoidable.

In many cases, the correct agentic action is refusal or escalation, not output. The value of an agentic system is not that it can generate, but that it knows when it should not.

Why This Matters to Senior Leadership

For leaders responsible for credibility, the primary risk of agentic AI is not technical failure. It is ungoverned success. Systems that move faster than oversight can absorb create exposure that compounds quietly.

The opportunity, however, is substantial. Well-designed agentic workflows reduce manual drag, surface meaningful signal earlier, and preserve human judgment for decisions that actually matter. They also create durable institutional assets – documented reasoning, consistent standards, and reusable analysis that survives turnover and scrutiny.

This is how legacy organizations scale without eroding trust.

Elevator Pitch (Agentic Workflows):

We are not automating decisions. We are structuring workflows where agents gather, analyze, and validate information under clear rules, while humans remain accountable for every consequential call. The goal is reliability, clarity, and trust – not speed for its own sake.”

Agentic AI will not transform legacy organizations through ambition alone. It will do so through discipline. The institutions that succeed will not be the ones that adopt the most autonomy the fastest. They will be the ones that prove, step by step, what agents can do responsibly today. Less theory. More practice. And accountability at every turn.

From Real-World Archives to Agentic Creative Engines

Velocity Ascent Live · September 1, 2025 ·

At Velocity Ascent, we see archives not as dusty vaults, but as raw material for future growth. By digitizing collections and pairing them with ethical AI, companies can unlock entirely new streams of value

Most organizations sit on archives that are larger than they realize – thousands, sometimes millions, of physical items stored away in boxes, warehouses, or filing cabinets. These collections often carry decades of history and brand equity, but in their current form, they’re static. Locked up. Untapped.

What if those same archives could power an entirely new creative and commercial engine?

Archives are not just dusty forgotten vaults of content, but are instead raw material for future growth. By digitizing collections and pairing them with ethical AI, companies can unlock entirely new streams of value: fresh brand imagery, licensing opportunities, and dynamic storytelling rooted in their own DNA.

Step One – Digitizing the Originals

The first step is practical: capture and catalog the physical assets. Think of this like a fashion house digitizing vintage textiles so they can be reused and reinterpreted. Using high-fidelity photography, scanning, and cataloging workflows, each item is preserved, protected, and made usable in modern systems. The result is a structured, searchable digital archive that’s more than just a reference library – it’s the foundation for everything that follows.

Step Two – Creating a Licensing Layer

Even before AI comes into play, a digitized archive creates immediate business value. Each digital object – whether a patch, photo, or piece of memorabilia – can be licensed on its own. That’s fabric by the yard, not just finished garments. It’s a scalable way to monetize collections that otherwise sit idle.

Step Three – Training the Creative Engine

Here’s where things accelerate. Once digitized, archives can be used to train lightweight AI models (known as LoRAs – Low-Rank Adaptations). In plain English, this is a way of teaching an existing AI model your unique style without starting from scratch. It’s faster, more cost-effective, and requires less computing power.

Imagine teaching a digital atelier to create in your brand’s house style. A collegiate archive, for example, can become the training ground for generating on-brand imagery that feels authentic and instantly recognizable.

Step Four – Generating New Assets

With the model trained, the archive transforms from static history to living creativity. The AI can generate fresh interpretations – new visuals, product concepts, or campaign assets – all rooted in the original DNA of the collection. It’s like hosting a modern runway show built from vintage patterns: heritage and innovation, combined.

Step Five – Building the Living Archive

Not every prototype belongs in circulation. That’s why we curate, filter, and validate the AI-generated outputs into a private, evolving library. This living archive becomes a source of brand-safe assets, owned outright by the organization, ready to be licensed or deployed

From Manual to Autonomous: Guardrails and Autonomy

We also see a role for agentic AI – systems that can act with autonomy inside defined guardrails. These agents handle repetitive tasks like watermarking, IP monitoring, and catalog enrichment, while humans stay in control of the big decisions. The archive doesn’t just sit there; it actively defends itself, learns, and surfaces new opportunities.

Instead of a tool that only responds when you ask, an agent can monitor, repeat, and adjust tasks proactively. But it doesn’t run wild: it follows rules we set, checks back when decisions matter, and works alongside people like a junior teammate who handles the busywork while flagging anything that needs human judgment.

Sample: Agentic Watermarking & IP Monitoring
Always-On Protection for Ethical Digital Assets

By embedding invisible digital watermarks into your ethical digital assets at the point of capture, we enable not only rights protection but also real-time tracking across digital platforms. A dedicated agent can monitor web traffic 24/7 – scanning social media, eCommerce sites, and marketplaces for unauthorized use of protected content.

When violations are detected, the system can automatically log the incident, generate a compliance report, and trigger a predefined enforcement workflow – such as alerting legal teams, issuing DMCA takedown notices, or notifying licensing partners.

This turns watermarking into a fully active layer of brand defense – protecting IP value while reducing manual oversight.

We have assembled a concise technical explanation of each of the leading protocols, followed by a simplified comparison table ranking them from most stable/general-use to most emerging.

MCP – Model Context Protocol

MCP is designed as a tightly structured, JSON-RPC-based client-server protocol that standardizes how large language models (LLMs) receive context and interact with external tools.

Think of it as the AI equivalent of USB-C: a unified plug-and-play standard for delivering prompts, resources, tools, and sampling instructions to models. It supports robust session lifecycles (initialize, operate, shut down), secure communication, and asynchronous notifications. It excels in environments where deterministic, typed data flows are essential – like plug-in platforms or enterprise tools with strict integration requirements. Its predictability and strong structure make it the go-to protocol for stable, general-purpose AI agent interactions today.

ACP – Agent Communication Protocol

ACP introduces REST-native, performative messaging using multipart messages, MIME types, and streaming capabilities. This protocol is best suited for systems that already speak HTTP and need richer communication models (text, images, binary data). It sits one layer above MCP – more flexible, more expressive, and excellent for multimodal or asynchronous workflows.

ACP allows agents to communicate through ordered message parts and typed artifacts, making it a better fit for web-native infrastructure and cloud-based multi-agent systems. However, it requires a registry and stronger orchestration overhead, which can introduce complexity.

A2A – Agent-to-Agent Protocol

Developed with enterprise collaboration in mind, A2A allows agents to dynamically discover each other and delegate tasks using structured Agent Cards. These cards describe each agent’s capabilities and authentication needs.

A2A supports both synchronous and asynchronous workflows through JSON-RPC and Server-Sent Events, making it ideal for internal task routing and coordination across teams of agents. It’s powerful in trusted networks and enterprise settings, A2A assumes a relatively static or known network of peers. It doesn’t scale easily to open environments without added infrastructure.

ANP – Agent Network Protocol

ANP is the most decentralized and future-leaning of the protocols. It relies on Decentralized Identifiers (DIDs), semantic web principles (JSON-LD), and open discovery mechanisms to create a peer-to-peer network of interoperable agents. The Agents describe themselves using metadata (ADP files), enabling flexible negotiation and interaction across unknown or untrusted domains.

ANP is foundational for agent marketplaces, cross-platform ecosystems, and long-term visions of the “Internet of AI Agents.” Its trade-off is stability – it’s complex, requires DID infrastructure, and is still maturing in practice.

Why does this matter to the C-Suite?

Think of it as the difference between keeping an archive in cold storage versus letting it fuel an always-on creative engine.

This isn’t about chasing trends. It’s about creating an ethical, brand-native creative pipeline. Every asset is traceable back to the original archive. Every new image is born from your existing brand DNA. This ensures integrity while also opening the door to limited drops, digital collectibles, or new licensing categories that simply weren’t possible before.

Elevator Pitch: From Archive to Agentic Creative Engine

Transform static collections into living assets – digitized, licensed, and powered by ethical AI – generating new revenue and brand-safe imagery.

We turn static archives into living creative engines. By digitizing collections and training ethical AI models on your unique assets, we unlock new revenue through licensing and generate brand-safe imagery rooted in your own DNA.