When we look into the general use cases beyond Web3, many firms from large to small have already started implementing AI agents into their daily operations—sales, marketing, finance, legal, IT, project management, logistics, customer service, workflow automation—basically anything imaginable.

We have transitioned from humans manually crunching numbers, performing repetitive tasks, and filling out Excel sheets to having digital workers (AI Agents) that operate autonomously 24/7. These agents are not only more efficient but also significantly cheaper.

Web2 companies are willing to pay between $50K - $200K or more for AI-driven sales and marketing agents. Many agent providers operate highly profitable businesses, leveraging either a SaaS subscription model or a consumption-based model (charging per token usage).

AI Agent Use Cases in Web2: Insights from YC Startups

• @Apten_AI – AI + SMS agent that facilitates the sales/marketing process.

• @Bild_AI – Reads construction blueprints, extracts materials/spec data, and estimates costs based on the gathered data.

• @Casixty – Marketing agent that identifies trending topics on Reddit, automates responses, and increases brand engagement. Imagine this product applied to CT!

These examples showcase how AI agents are already transforming traditional industries, automating manual tasks, and optimizing workflows. While Web2 companies have rapidly adopted AI-powered agents, the Web3 space has also begun to embrace this technology—but with a key difference.

Instead of focusing solely on operational efficiency, Web3 AI agents integrate with blockchain technology to unlock entirely new use cases.

Web3 AI Agents: Beyond Yapping Slop Bot

A few months ago, most Web3 agents were simply conversational bots on Twitter. However, the landscape has evolved significantly. These agents are now integrating with various tools and plugins, allowing them to perform more complex operations.

• @sendaifun – Solana AI Agent Kit enabling actions from basic token management to complex DeFi operations.
• @ai16zdao – Integrated with 100+ plugins, ranging from social media interactions to automated trading and DeFi operations.
• @Cod3xOrg, @Almanak__ – No-code infrastructure that allows users to create autonomous trading agents.
• @gizatechxyz – Autonomous DeFi assistant tailored for investors.

With DeFi being the largest sector in Crypto (> $100B TVL), the most impactful crypto-native AI agent use cases fall under DeFAI.

AI agents in DeFi are not just about simplifying complex experiences through NLP interfaces. They also leverage on-chain data to unlock new opportunities.

Blockchain provides a wealth of structured data—credentials, transaction history, PnL, governance activities, and lending/borrowing patterns. AI can process, analyze, and extract insights from this data to automate workflows and enhance decision-making.

Web2 Vertical Agents Powered by Crypto Rails

We’re also witnessing the emergence of Web2 vertical agents integrating crypto-native models. A prime example is @virtuals_io launching on Solana.

• @_PerspectiveAI – AI-powered fact-checking, continuously improved by community input.

• @Roboagent69 – Acts as a personal assistant, booking flights, taxis, ordering groceries, and scheduling meetings.

• @HeyTracyAI – AI-driven sports commentary and analytics, starting with the NBA.

Unlike SaaS models, these agents often rely on token-gating, where users must stake/hold a certain amount of tokens for premium access while maintaining free basic-tier access. Revenue is generated through token trading fees and API usage.

Can Web3 AI Agents Compete with Web2 Startups?

In the short term, Web3 teams face challenges in finding Product-Market Fit (PMF) and achieving meaningful adoption. They need consistent revenue streams of at least $1M-$2M ARR to compete effectively. However, in the medium to long term, Web3 models have inherent advantages:

• Community-driven growth through token incentives and alignment.
• Global liquidity and accessibility, with decentralized and non-custodial platforms removing barriers to adoption.

Additionally, the rise of DeepSeek and interest from Web2 AI talent in open-source AI are further accelerating Crypto x AI synergies.

Key Crypto-native AI Agent Use Cases

1. DeFAI – Abstraction layers, autonomous trading agents, and staking/lending/borrowing solutions that serve as frontends for DeFi infrastructure as well as enhancing the efficiency of Defi products.

2. Research & Reasoning Agents – AI-powered research co-pilots that analyze data, weed out noise, and generate actionable insights. My fav recently have been the security agents e.g.

• @soleng_agent – DevRel agent analyzing GitHub repos.
• @CertaiK_Agent – AI-based auditing service identifying potential threats (soon offering Agent Scoring System)

1. Data-driven AI Agents – Leveraging on-chain and social data to power autonomous decision-making and execution.

These three verticals represent the most promising areas for crypto-native AI agents.

Bottom Line: Where We Stand and What’s Next

The market has been consolidating for over a month, with altcoins and agent-related tokens experiencing major pullbacks. However, we’re reaching a stage where token fundamentals are becoming clearer.

Web2 vertical agents have already proven their value, with companies willing to pay substantial amounts for AI-powered automation. Meanwhile, Web3 vertical agents are still in their early stages, but their potential is massive. By combining token-based incentives, decentralized access, and deep integrations with blockchain data, Web3 AI agents have the opportunity to evolve beyond their Web2 counterparts.

The fundamental question remains: Will Web3 vertical agents reach adoption levels comparable to Web2, or will they redefine the landscape entirely by leveraging blockchain-native advantages?

As vertical AI agents continue to develop in both Web2 and Web3, the lines between them may blur. The teams that can successfully merge the best aspects of both—leveraging AI’s efficiency and blockchain’s decentralization—will likely shape the next generation of automation and intelligence in the digital economy.

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