Title: Vertical Agents: The Crypto-Native Agent Use Cases
Author: 0xJeff (@Defi 0xJeff)
Compiled by: Asher (@Asher_0210)
When observing use cases outside of Web3, many companies, both large and small, have already started integrating AI agents into their daily operations, covering sales, marketing, finance, legal, IT, project management, logistics, customer service, workflow automation, and almost all other areas. This transformation has made manual data processing, repetitive task execution, and Excel spreadsheet filling unnecessary, replaced by fully automated, 24/7 digital workers (AI agents). These agents not only significantly improve efficiency, but also significantly reduce costs.
In the Web2 domain, companies are willing to pay $50,000 to $200,000 or more for AI-driven sales and marketing agents. Many agent providers operate highly profitable businesses through SaaS subscription models or usage-based consumption models.
AI Agent Use Cases in Web2: Insights from YC Startups
Apten is an AI-driven SMS and voice agent designed to automate sales and marketing for B2C enterprises. It can simulate human communication and seamlessly switch between SMS and voice.
Bild AI can read architectural blueprints and extract relevant material and cost data. This technology helps builders and contractors avoid the high costs of construction plan errors, reducing unnecessary work and time consumption.
Casixty is a marketing agent that can identify trending topics on Reddit in real-time, scan and analyze website content, and then push marketing opportunities based on user intent to users' Slack channels.
These examples demonstrate how AI agents can effectively transform traditional industries, automate tedious manual tasks, and optimize workflows. In the Web2 domain, many companies have quickly adopted AI-driven agents to improve operational efficiency. However, the adoption in the Web3 domain has a significant difference: Web3 AI agents not only focus on improving operational efficiency, but also open up new use cases and possibilities through the integration with blockchain technology.
Web3 AI Agents: Beyond Chatty Spam Bots
A few months ago, most Web3 agents were just chatbots on X platform. However, the situation has changed significantly. These agents are now integrating with various tools and plugins, enabling them to perform more complex operations, such as:
SendAI: Solana AI Agent Kit makes it possible to manage basic tokens to complex DeFi operations;
ai16z: Integrated with over 100 plugins, covering social media interactions, automated trading, and decentralized finance operations;
Cod3x, Almanak: No-code infrastructure allowing users to create autonomous trading agents;
Giza: Autonomous DeFi assistant customized for investors.
As DeFi has become the largest segment in the cryptocurrency space, with a total locked value exceeding $100 billion, the most impactful crypto-native AI agent use cases have also emerged in the DeFi domain, forming the so-called DeFAI.
In DeFi, AI agents not only simplify complex user experiences through natural language processing (NLP) interfaces. They also leverage on-chain data to unlock new opportunities and improve efficiency. Blockchains provide a wealth of structured data, including credentials, transaction histories, profit and loss records, governance activities, and lending patterns. AI agents can process and analyze this data to extract deep insights, enabling automated workflows and enhanced decision-making capabilities.
By combining blockchain and artificial intelligence, DeFi AI agents can more accurately identify market trends, optimize trading strategies, and enhance risk management, further driving innovation and development in decentralized finance.
Web2 Vertical Agents Powered by Crypto-Native Infrastructure
We are also witnessing the emergence of Web2 vertical agents integrating crypto-native models, a typical example being Virtuals Protocol launching on the Solana ecosystem.
Perspective AI: AI-based fact-checking, continuously improving through community feedback;
R6D9: Serving as a personal assistant, booking flights, taxis, ordering groceries, and scheduling meetings;
HeyTracyAI: AI-based sports commentary and analysis, starting with the NBA.
Unlike the SaaS model, these agents typically rely on token gating, where users must stake/hold a certain amount of tokens to access premium features, while maintaining free basic-tier access. Revenues are generated through token transaction fees and API usage.
Can Web3 AI Agents Compete with Web2 Startups?
In the short term, Web3 teams face the challenge of finding product-market fit and achieving meaningful adoption, requiring at least $1-2 million in annual recurring revenue to compete effectively. However, in the medium to long term, the Web3 model has inherent advantages:
Community-driven growth through token incentives and alignment;
Global liquidity and accessibility, with decentralized and non-custodial platforms eliminating adoption barriers.
Furthermore, the rise of DeepSeek and the interest of Web2 AI talents in open-source AI further accelerate the synergy between crypto and AI.
Currently, the crypto-native AI agent use cases include:
DeFAI: Abstraction layer, automated trading agents, staking/lending solutions, serving not only as DeFi infrastructure frontends but also enhancing the efficiency of DeFi products.
Research & Reasoning Agents: AI-driven research assistance tools, analyzing data, filtering out noise, and generating actionable insights. My recent favorite is security agents, such as:
SOLENG: Developer relationship agent analyzing GitHub codebases;
BevorAI: AI-based audit service identifying potential threats (agent scoring system to be launched soon).
Data-Driven AI Agents: Leveraging on-chain and social data to drive autonomous decision-making and execution. These three areas represent the most promising application directions for crypto-native AI agents.
Summary
Over the past month, the market has been in a consolidation phase, and while tokens related to agents and some smaller cryptocurrencies have experienced significant corrections, we are gradually entering a period with clearer token fundamentals.
Web2 vertical agents have already proven their value, with many companies willing to pay high fees for AI-driven automation. In comparison, Web3 vertical agents are still in the early stages, but their potential is immense. By combining token-based incentive mechanisms, decentralized access rights, and deep integration with blockchain data, Web3 AI agents may surpass Web2 agents in the future. However, the key question remains: Can Web3 vertical agents achieve market acceptance comparable to Web2, or can they completely reshape the market landscape by fully leveraging the native advantages of blockchain technology?
As the vertical AI agents in Web2 and Web3 evolve, the boundaries between the two may become blurred. Teams that can successfully combine the advantages of both, leveraging the efficiency of AI and the decentralized nature of blockchain, are likely to lead the next generation of automation and intelligence in the digital economy.
