ND Labs | Web3 Solutions Built at AI Speed
type: channel
description: White label services for Web3 businesses
date: 2026-07-01T16:09:28Z
update period: hourly
update frequency: 1
update base: 2000-01-01T12:00+00:00
generator agent: https://wordpress.org/
items: Guide to AI Agentic Workflow Design: How to Automate Web3 Safely
items: What Is Agentic Architecture? Key Components, Workflows and Web3 Examples
items: Agentic AI Security: Risks, Guardrails and Best Practices for Web3
items: Agentic AI Examples: 12 Use Cases for Web3 and Business
items: Agentic AI vs Generative AI: What’s the Difference?
items: Best Web3 Development Companies in 2026: Top Global Agencies Compared
items: From AI Hype to AI Integration: Key Takeaways from Infoshare for Web3 Founders
items: Designing a NIL Marketplace for Fan Loyalty
items: What Is NFT Art? A Complete Beginner’s Guide (2026 Update)
items: Exploring the power of the Real Estate Tokenization in 2026

Guide to AI Agentic Workflow Design: How to Automate Web3 Safely
type: item
creator: Dmitry K.
date: 2026-07-01T15:58:41Z
subject: Blockchain
description: When implementing artificial intelligence, Web3 companies often ask the wrong question: “Which model should we choose?” At ND Labs, we believe the ultimate question should be: “Which specific business workflow can an AI agent safely support?” The agent is not the product; the workflow is. This is why successful AI deployment doesn’t start with building […] The post Guide to AI Agentic Workflow Design: How to Automate Web3 Safely appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: When implementing artificial intelligence, Web3 companies often ask the wrong question: “Which model should we choose?” At ND Labs, we believe the ultimate question should be: “Which specific business workflow can an AI agent safely support?” The agent is not the product; the workflow is. This is why successful AI deployment doesn’t start with building a generic, sci-fi “AI assistant.” Instead, it begins with one focused product process that is easy to map, test, monitor, and scale. If you are still exploring the core differences between basic prompts and fully autonomous systems, start with our deep dive into Agentic AI vs Generative AI. This guide focuses entirely on the practical layer: how to design a safe, highly-optimized AI agentic workflow tailored for the unique challenges of the Web3 ecosystem. What is an AI Agentic Workflow? AI agentic workflow automation means using specialized autonomous software to execute multi-step, complex processes inside a digital product. Unlike traditional automation (like Zapier or rigid RPA scripts) that simply moves data from point A to point B using strict “if-this-then-that” rules, an agentic workflow introduces reasoning, context evaluation, and dynamic tool selection at every turn. A resilient agentic workflow follows a strict, traceable path engineered into the system: Trigger → Inputs → AI Reasoning Loop → Tool Execution → Verification → Human-in-the-Loop Approval → Output & Logs The core philosophy behind this design is predictability. By structuring the workflow layer correctly, we define exactly what data the agent can read, which smart contracts it can analyze, and precisely where the system must pause to wait for human verification. The Spectrum of Autonomy: From Reading to Acting To avoid unnecessary risks, especially when handling on-chain data, we categorize agentic workflows into four distinct tiers. For Web3 applications, we heavily advise starting with lower-risk tiers before unlocking fully autonomous capabi…

What Is Agentic Architecture? Key Components, Workflows and Web3 Examples
type: item
creator: Dmitry K.
date: 2026-06-30T10:21:39Z
subject: Blockchain
description: Agentic architecture is the system design that allows AI agents to plan tasks, use tools, access memory, coordinate workflows, request approval, and complete goals with controlled autonomy. It defines how an AI system moves from a user request to a real result: which agent acts, which tools it can use, what data it can access, […] The post What Is Agentic Architecture? Key Components, Workflows and Web3 Examples appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: Agentic architecture is the system design that allows AI agents to plan tasks, use tools, access memory, coordinate workflows, request approval, and complete goals with controlled autonomy. It defines how an AI system moves from a user request to a real result: which agent acts, which tools it can use, what data it can access, or when it should stop. Building a robust technical infrastructure is only half the battle. Before engineering the database models and LLM routing layers, the product team must define the boundaries of the underlying business logic. To see how to map these processes step-by-step, review our framework on blueprints and safety controls for AI agentic workflow automation. If you are comparing agentic AI and generative AI, start with our guide to Agentic AI vs Generative AI. This article goes one layer deeper and explains how agentic systems are structured. What is agentic architecture in AI? Agentic architecture is the structure behind AI agent systems. It connects the model, planning logic, memory, tools, agent roles, approval rules, and monitoring into one workflow. The goal is to help the system complete a task in a controlled way. For example, an agentic system can review a smart contract audit, identify high-risk findings, compare them with an internal checklist, prepare a summary for developers, and flag issues that need manual review. A well-designed agentic architecture usually includes: an LLM or another reasoning model; goal interpretation; planning logic; memory and context; tools and APIs; orchestration; agent roles; evaluation; approval controls; monitoring and logs. Without these parts, an AI agent often becomes a chatbot with extra features. Why agentic architecture matters for production AI systems Most companies need agents that can help with real work. In production, the answers to these questions matter because they define whether an agentic system is usable, safe, and reliable. What can the agent do? Which tools can it use? Wh…

Agentic AI Security: Risks, Guardrails and Best Practices for Web3
type: item
creator: Dmitry K.
date: 2026-06-23T17:27:06Z
subject: Blockchain
description: In this article, we explain the biggest agentic AI security risks, how they apply to Web3, and what guardrails teams need before putting AI agents near wallets, smart contracts, transactions, or user assets. If you are still comparing agentic AI and generative AI concepts, start with our guide to agentic AI vs generative AI. If […] The post Agentic AI Security: Risks, Guardrails and Best Practices for Web3 appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: In this article, we explain the biggest agentic AI security risks, how they apply to Web3, and what guardrails teams need before putting AI agents near wallets, smart contracts, transactions, or user assets. If you are still comparing agentic AI and generative AI concepts, start with our guide to agentic AI vs generative AI. If you want to see practical scenarios first, read our guide to agentic AI examples. What Is Agentic AI Security? Agentic AI security is the practice of protecting AI systems that can pursue goals, use tools, remember context, make decisions, and take or prepare actions. It means securing the entire agentic workflow, not only the model output. An agentic AI system may: read private or business data; retrieve context from memory or RAG; call APIs; use plugins, connectors, or MCP servers; prepare code changes; trigger workflows; interact with wallets; prepare transactions; communicate with other agents; ask a human for approval; continue a task over many steps. Each of these abilities creates a new security surface. An agentic system needs: identity controls, permission scoping, tool governance, environment separation, audit logs, approval workflows, and runtime monitoring. For Web3, the stakes are higher because many actions are financial and often irreversible. If a wallet agent prepares a bad transaction and the user signs it, the loss may not be easy to reverse. If a DAO governance agent summarizes a proposal incorrectly, voters may make a poor decision. If a smart contract monitoring agent misses a risky event, the team may react too late. Agentic AI security is about making sure the agent can help without taking more power than it needs. Why Agentic AI Security Is Different from Chatbot Security An agentic system follows a longer path. For example, the user gives a goal, the agent retrieves context, checks memory, chooses tools, calls an API, reads the results, plans the next step, and prepares an action. Every step in this chain can fail or…

Agentic AI Examples: 12 Use Cases for Web3 and Business
type: item
creator: Dmitry K.
date: 2026-06-22T19:32:30Z
subject: AI
subject: Blockchain
subject: Wallet development
description: In Web3, a user may need to understand wallet activity, token risk, gas fees, smart contract warnings, bridge options, DeFi positions, or DAO proposals. They involve live data, user assets, contracts, and security decisions. And an AI agent should help users understand what is happening, prepare the next step, warn them about risk, and keep […] The post Agentic AI Examples: 12 Use Cases for Web3 and Business appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: In Web3, a user may need to understand wallet activity, token risk, gas fees, smart contract warnings, bridge options, DeFi positions, or DAO proposals. They involve live data, user assets, contracts, and security decisions. And an AI agent should help users understand what is happening, prepare the next step, warn them about risk, and keep the final decision in human hands. These agentic AI examples show how AI agents can support real workflows. The focus here is Web3, DeFi, wallets, DAOs, smart contracts, and business operations. Some examples below describe current ND Labs product direction. Others show practical Web3 agentic AI workflows that teams can design around the principles: trusted data, tool access, human approval, and guardrails. If you are still comparing agentic AI with generative AI, start with our guide to agentic AI vs generative AI. Exploring how AI agents could fit into your Web3 product? ND Labs can help you map safe wallet, DeFi, and on-chain workflows before development starts. AI agent development for Web3 Real Web3 Agentic AI Examples Already Emerging Web3 agentic AI is still earlier than enterprise agentic AI, but real examples are already emerging. They show the same pattern: agents need trusted data, tool access, validation, limits, and observability. In Web3, those controls matter even more because agents may work near real capital. One of the strongest public examples is DX Terminal Pro, a 21-day deployment of on-chain language-model agents operating under real capital. In this deployment, thousands of user-funded agents traded real ETH in a bounded on-chain market. Users configured vaults through structured controls and natural-language strategies, while agents chose buy and sell actions within defined limits. Coinbase x402 is another important example is agentic payments. Coinbase x402 is an open payment standard that brings the HTTP 402 “Payment Required” flow into crypto and stablecoin payments. The idea is simple: software, includ…

Agentic AI vs Generative AI: What’s the Difference?
type: item
creator: Dmitry K.
date: 2026-06-19T14:01:54Z
subject: Blockchain
description: Generative AI creates content. Agentic AI gets work done. That is the simplest way to separate them. Generative AI can write an email, summarize a report, create code, explain a smart contract, or draft a marketing page. It responds to a prompt and gives you an output. Agentic AI goes further. It can take a […] The post Agentic AI vs Generative AI: What’s the Difference? appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: Generative AI creates content. Agentic AI gets work done. That is the simplest way to separate them. Generative AI can write an email, summarize a report, create code, explain a smart contract, or draft a marketing page. It responds to a prompt and gives you an output. Agentic AI goes further. It can take a goal, break it into steps, use tools, check data, make decisions, ask for approval, and complete a workflow. If you need text, ideas, summaries, or code suggestions, generative AI may be enough. If you need a system that monitors data, reacts to changes, uses APIs, checks risk, or prepares actions, you are moving into agentic AI. For Web3 teams, the gap is even bigger. A generative AI tool can explain a DeFi strategy. An agentic AI system can monitor on-chain data, compare protocol risk, simulate a transaction, ask for approval, and log what happened. What Is Generative AI? Generative AI is AI that creates new content from a prompt. You ask it for something. It gives you something back. That output can be text, code, an image, a summary, a table, a product description, or a support reply. The model looks at patterns in its training data and the context you give it. Then it generates a response. For example, a product manager can ask generative AI to draft a feature brief. A developer can ask for a code suggestion. A marketer can ask for five headline options. A support team can ask it to rewrite a reply in a clearer tone. Generative AI is useful when the task is mostly about language, structure, or content. It can help you move faster. It can reduce blank-page work. It can turn rough notes into a clear draft. It can explain a technical concept in plain English. It can also summarize long documents so a team does not have to read everything from scratch. But generative AI usually waits for the next prompt. It does not naturally own the whole workflow. It does not decide what step should happen next unless you ask. It does not automatically check a live database, c…

Best Web3 Development Companies in 2026: Top Global Agencies Compared
type: item
creator: Dmitry K.
date: 2026-05-29T16:09:55Z
subject: Blockchain
subject: blockchain development
subject: web3
description: A few years ago, many companies needed only a basic smart contract, an NFT drop, or a simple token dashboard. In 2026, expectations are much higher. Founders, CTOs, and enterprise teams are building decentralized applications, tokenization platforms, DeFi products, blockchain infrastructure, AI-powered Web3 tools, gaming economies, and secure on-chain workflows. That shift has changed what […] The post Best Web3 Development Companies in 2026: Top Global Agencies Compared appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: A few years ago, many companies needed only a basic smart contract, an NFT drop, or a simple token dashboard. In 2026, expectations are much higher. Founders, CTOs, and enterprise teams are building decentralized applications, tokenization platforms, DeFi products, blockchain infrastructure, AI-powered Web3 tools, gaming economies, and secure on-chain workflows. That shift has changed what it means to hire a Web3 agency. The best web3 development companies are no longer just Solidity teams. They need to understand product strategy, smart contract security, backend systems, wallet integrations, token standards, user experience, cloud infrastructure, compliance risks, and post-launch support. In short, they need to build products that real users can trust. This guide compares 10 leading global Web3 agencies and technology companies based on services, expertise, delivery model, security focus, reputation, and project fit. It is written for teams that want a practical vendor comparison rather than a promotional list. Methodology: How These Companies Were Compared This ranking is based on a practical buyer’s perspective rather than brand awareness alone. A well-known company is not always the right fit for every project, and a smaller agency may be better suited for a startup that needs speed, flexibility, and product guidance. We reviewed each company using the following criteria. Technical Expertise We looked at experience across major blockchain ecosystems, including Ethereum, Solana, Polygon, Arbitrum, Base, Avalanche, Cosmos, and Layer-2 networks. We also considered whether a company focuses only on application development or can support more complex architecture. Service Breadth The strongest vendors usually provide more than smart contract development. They may also offer blockchain consulting, dApp development, backend engineering, UX/UI design, wallet integration, NFT marketplace development, tokenization, DeFi development, QA, DevOps, and post-launch support. S…

From AI Hype to AI Integration: Key Takeaways from Infoshare for Web3 Founders
type: item
creator: Dmitry K.
date: 2026-05-21T14:01:18Z
subject: Blockchain
subject: Events
subject: blockchain
subject: web3
description: At the Infoshare conference, one thing became clear: AI is no longer just a topic for innovation panels, experimental demos, or future-looking discussions. As a Community Partner of Infoshare 2026, ND Labs was glad to support a conference that brings together technology leaders, founders, engineers, investors, and companies shaping the future of digital products. After […] The post From AI Hype to AI Integration: Key Takeaways from Infoshare for Web3 Founders appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: At the Infoshare conference, one thing became clear: AI is no longer just a topic for innovation panels, experimental demos, or future-looking discussions. As a Community Partner of Infoshare 2026, ND Labs was glad to support a conference that brings together technology leaders, founders, engineers, investors, and companies shaping the future of digital products. After attending the event, ND Labs’ CEO Dmitriy Khanevich noted that AI was not treated as a separate innovation track anymore. It appeared across infrastructure, cybersecurity, product development, and business strategy discussions, which is a clear signal for Web3 founders building the next generation of digital products. The conversation is moving from “How can we use AI?” to a much more important question: How can we integrate AI safely, reliably, and meaningfully into real products? AI Is Becoming Infrastructure, Not Just a Feature One of the strongest signals from Infoshare was that AI is moving deeper into the infrastructure layer. Behind every simple AI-powered API, there is a complex technical reality: GPUs, latency, cold starts, routing, inference costs, model availability, and scalability. For users, AI often looks like a simple interface. For product teams, it is a system that needs architecture, cost control, reliability, and performance planning. This is especially important for Web3 founders. Web3 products already operate in environments where performance, trust, and security matter. Wallets, DeFi platforms, RWA solutions, analytics tools, DAO infrastructure, and decentralized applications cannot treat AI as a simple plug-in. If AI becomes part of the user experience, the product team must answer critical questions: Can the system scale when usage grows? What happens when AI latency affects a key user flow? How much does each AI interaction cost? Where does user data go? Can the output be trusted? This is also why AI-augmented Web3 development is becoming more relevant for founders: AI can sp…

Designing a NIL Marketplace for Fan Loyalty
type: item
creator: Dmitry K.
date: 2026-05-19T14:07:05Z
subject: Blockchain
description: How ND Labs turned Fan Passes, athlete discovery, Vault ownership, and squad-based rewards into a mobile-first NIL marketplace experience for women’s collegiate sports. The NIL economy has changed how college athletes, brands, fans, universities, and communities interact. Since student-athletes gained the ability to monetize their name, image, and likeness, the market has moved from basic […] The post Designing a NIL Marketplace for Fan Loyalty appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: How ND Labs turned Fan Passes, athlete discovery, Vault ownership, and squad-based rewards into a mobile-first NIL marketplace experience for women’s collegiate sports. The NIL economy has changed how college athletes, brands, fans, universities, and communities interact. Since student-athletes gained the ability to monetize their name, image, and likeness, the market has moved from basic sponsorship matching toward more advanced digital platforms. Today, a NIL marketplace can help athletes become discoverable, connect with brands, receive fan support, sell digital products, unlock community experiences, and build long-term monetization channels. But the next product question is not only how athletes get paid. How do you turn one-time NIL support into a repeatable fan engagement experience? That was the core idea behind the NIL Marketplace prototype designed by ND Labs — a mobile-first sports fintech concept for women’s collegiate sports. The product explores how fans can discover athletes, purchase Fan Passes, unlock exclusive content, store passes in a personal Vault, and build squads to earn loyalty rewards. In other words, the prototype was not designed as a simple digital collectibles marketplace. It was designed as a fan economy. You can view the full visual case study here: NIL Marketplace Case Study by ND Labs. What Is a NIL Marketplace? A NIL marketplace is a digital platform where college athletes can monetize their name, image, and likeness by connecting with brands, fans, businesses, donors, universities, collectives, and other partners. In a traditional NIL marketplace, athletes may create profiles, showcase their audience, list available opportunities, receive sponsorship requests, sell merchandise, book appearances, or participate in paid content campaigns. In a fan-driven NIL marketplace, the model can go further. Fans may be able to support athletes directly through digital products, memberships, personalized content, community access, exclusive exp…

What Is NFT Art? A Complete Beginner’s Guide (2026 Update)
type: item
creator: Dmitry K.
date: 2026-05-07T16:16:00Z
subject: Blockchain
subject: NFT
subject: NFT collections
subject: NFT for Beginners
description: Introduction Since the introduction of the non-fungible token concept, the NFTs were touted as a new way for digital creators to express themselves while protecting their digital ownership, allowing artists and brands to share and protect their work as never before. NFTs became one of the most significant and resilient contemporary innovations in tech, finance, […] The post What Is NFT Art? A Complete Beginner’s Guide (2026 Update) appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: Introduction Since the introduction of the non-fungible token concept, the NFTs were touted as a new way for digital creators to express themselves while protecting their digital ownership, allowing artists and brands to share and protect their work as never before. NFTs became one of the most significant and resilient contemporary innovations in tech, finance, art, fashion, sports, and memes. Most crypto enthusiasts consider it the future of digital art, offering artists new ways of earning from their creations. Let’s dig deeper into the topic of NFTs to understand what it is, how to use it and how to benefit from it. What is NFT? NFT stands for a non-fungible token, meaning it’s unique and irreplaceable. The main difference between an NFT and cryptocurrency is trading value. NFTs can’t be exchanged for equal value for each other because they possess a unique digital signature. Anything, including digital files, mp3s, or MP4 video files that are tokenized and stored on the digital ledger. Practically, any digital file can be an NFT, for example, a viral meme or even the first-ever posted tweet or street art work. NFTs are bought and sold on the NFT marketplaces like Open Sea, Rarible, Nifty Gateway, and others, which usually function as digital auction houses where bidders can suggest their price and pay with cryptocurrency, for example, Bitcoin, Ethereum or Dogecoin. As most of the marketplaces are built on Ethereum, Ether is the most common cryptocurrency used for purchasing non-fungible tokens. What is NFT Art? Basically, NFT art is a digital representation of an item that can be traded, used, or stored on a blockchain. NFT only exists on blockchain, where the ownership of it is recorded. When NFTs are sold for money, this means there are new opportunities for artists and collectors to own the work they create. Almost anything can fall into the NFT art category, from the regular graphic art to GIFs, animations, music and emojis. With the power of smart contracts…

Exploring the power of the Real Estate Tokenization in 2026
type: item
creator: Dmitry K.
date: 2026-05-06T13:57:00Z
subject: Blockchain
description: Introduction In recent years, a groundbreaking innovation has emerged in the real estate industry that is reshaping the way we perceive property ownership and investment. This disruptive force is none other than real estate tokenization, a technology that leverages blockchain and cryptocurrencies to unlock new opportunities and efficiencies in the market. Tokenization, in essence, involves […] The post Exploring the power of the Real Estate Tokenization in 2026 appeared first on ND Labs | Web3 Solutions Built at AI Speed.
encoded: Introduction In recent years, a groundbreaking innovation has emerged in the real estate industry that is reshaping the way we perceive property ownership and investment. This disruptive force is none other than real estate tokenization, a technology that leverages blockchain and cryptocurrencies to unlock new opportunities and efficiencies in the market. Tokenization, in essence, involves the process of transforming real estate assets into digital tokens. These tokens represent fractional ownership rights or shares in a property, enabling investors to buy, sell, and trade these tokens on secure and transparent blockchain platforms. By breaking down large real estate assets into smaller, more affordable units, tokenization democratizes access to lucrative investment opportunities that were previously reserved for institutional players or wealthy individuals. This article delves into the world of real estate tokenization, exploring its underlying technology, benefits, and potential implications for the industry. We will examine how tokenization enhances liquidity, improves transparency, reduces costs, and unlocks previously untapped markets. What is tokenization of real estate? The tokenization of real estate involves converting physical properties into digital tokens using blockchain technology. These tokens represent fractional ownership shares in the property and can be bought, sold, and traded on blockchain platforms. This process brings several benefits to the real estate industry. Firstly, it enhances liquidity by enabling faster and more efficient transactions. Tokenization eliminates the need for intermediaries, reducing paperwork and costs associated with traditional real estate transactions. Transparency is another advantage. Blockchain’s immutable nature ensures that all ownership details and transactions are recorded and easily accessible. This transparency builds trust among investors and helps prevent fraudulent activities. Real estate tokenization also…