Interpretation of Y Combinators Spring Startup Guide: Six AI Agent Tracks Layout Future Startup Trends

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المؤلف الأصلي: 0x Jeff

الترجمة الأصلية: TechFlow

Y Combinator recently released a Request for الشركات الناشئة for Spring 2025, listing the areas they hope more entrepreneurs will focus on. These ideas reflect the emerging trend of AI Agents in Web2, focusing on solving practical problems and pain points, including:

AI App Store
Data Center
Compliance and audit tools
DocuSign 2.0 (Next Generation Electronic Signature Solution)
Browser and PC Automation أداةس
AI Personal Assistant
Devtools
The Future of Software Engineering (Engineering Agents)
AI Commercial Open Source Software
Agents that optimize code for hardware
Business-to-Agent (B2A)
Vertical AI Agents (Agents focused on specific industries or scenarios)
Reasoning AI infrastructure (technical foundation that supports efficient reasoning and operation of AI models)

These directions contain a lot of information, but if you have been deeply involved in this field, you will find that many Web3 intelligent agent teams have already made arrangements in these areas.

If you want to dive deeper into these trends, check out the original post by @ycombinator :

I believe the following areas will become key trends in the development of Web3 AI agents (in no particular order):

AI Commercial Open Source Software
Devtools
Vertical AI Agents
AI Personal Assistant
AI App Store
B2A (Business-to-Agent)

1. AI Commercial Open Source Software

Web3 AI has a natural connection with open source AI, which makes the open source field an important driving force for Web3. Take @ai16z dao as an example. They have promoted one of the largest open source AI movements. The ElizaOS framework launched has currently received 14k stars and 4,227 forks on GitHub. Despite market fluctuations, the adoption rate of this framework is still steadily increasing.

This open source movement also encourages Web3 developers to open source their own technologies, and promotes teams to develop AI technologies and frameworks so that other developers can collaborate more efficiently. In recent years, we have seen many open source frameworks that go beyond ElizaOS emerge, such as @arcdotfun , @GAME_Virtuals , @sendaifun , @pippinlovesyou ، و @freysa_ai , which together promote the development of the open source innovation ecosystem.

With the rapid development of AI agents, such as o3 launched by OpenAI and new models released by DeepSeek, and the accelerated launch of related products by technology giants, the demand for open source AI and Web3 AI is heating up. The combination of تشفيرcurrency and AI (Crypto x AI) is expected to occupy an important position in the AI market.

2. Devtools for AI Agents

Building AI agents is not just about creating intelligent models, but also about providing developers with efficient tools and infrastructure to help them turn these agents into practical applications. As AI agents become more complex, developers are rapidly growing in demand for friendly tools, frameworks, and platforms that can simplify the process of building, deploying, and managing agents.

In the Web2 era, the popularity of developer tools has significantly improved the capabilities of AI technology. Web3 has further promoted this trend, bringing new possibilities for AI development by introducing features such as decentralization, trustlessness, and open source collaboration. We are moving towards a new era in which the construction, iteration, and large-scale deployment of AI agents will no longer rely on the closed ecosystems of a few technology giants.

This trend has spawned many AI-oriented development platforms, intelligent agent ecosystems, and no-code/low-code tools. These tools are designed to lower the threshold for AI agent development and make it easy for more developers to participate.

In the Web3 space, more and more platforms are beginning to provide AI agent development toolkits to help developers quickly create and commercialize AI-based applications. Some noteworthy examples include:

@ai16z dao : Launching ElizaOS, which has the richest plug-ins and integrated functions.
@sendaifun : Solana Agent Kit, focusing on the development of intelligent agents on the Solana blockchain.
@CoinbaseDev : CDP Agent Kit, providing basic tools for on-chain AI agent development.
@autonolas : Launching Pearl, a utility-focused Agent App Store that provides services such as prediction markets, DeFi automation, and self-executing agents.
@AlloraNetwork : Provides machine learning infrastructure to help AI agents make more accurate predictions in real time.
@cookiedotfun : Focuses on AI-agent driven data analysis, helping agents extract social sentiment information from on-chain and off-chain data.
@getmasafi : Provides real-time data streaming solutions to provide AI agents with the latest dynamic intelligence.

Some no-code AI platforms focused on Web3 include:

@virtuals_io : The leading no-code/low-code AI agent building platform that helps developers quickly transform AI agents from concepts into actual products.
@HoloworldAI : A no-code platform focused on building 3D audio-visual AI agents, helping users design AI-driven virtual characters.
@Co d3 xOrg : A code-free tool designed specifically for automated trading agents to help traders automate trading strategies using AI.
@Almanak__ : A platform designed specifically for the development of institutional-level quantitative intelligent agents, supporting applications in advanced financial scenarios.
@EliteAgents_AI : Focuses on plug-in enhanced AI agents, seamlessly integrating with AI ecosystems such as ElizaOS and GAME.

Although the Web3 AI development tool ecosystem is still in its early stages, its infrastructure is improving rapidly. In the next few years, we can expect to see the formation of a fully decentralized AI development ecosystem. In this ecosystem, AI agents will become easier to build, while having full autonomy, scalability, and commercialization capabilities. The development tools that drive this transformation will become an indispensable infrastructure in the Web3 AI economy.

3. Vertical AI Agents

AI agents are evolving from general-purpose tools that perform simple tasks to highly specialized vertical domain agents. These agents focus on specific industries or scenarios and are able to handle complex and sophisticated tasks. By deepening domain knowledge, they are not only able to complete basic automation, but also act as decision-making agents to perform operations that require deep human expertise.

Today, the AI-driven verticalization trend is gradually emerging. In the fields of finance, law, scientific research, etc., intelligent agents have the ability to analyze, recommend, and even perform operations on behalf of users. This verticalization trend will further enhance the influence and application depth of AI agents in various industries.

Some typical examples of Vertical AI Agents include:

Tax Intelligence: Helps users calculate, optimize and execute tax saving plans.
Legal Agents: Able to review contracts and optimize terms, and can even participate in legal disputes on behalf of users.
Financial Agent: Analyze financial statements, interpret macroeconomic trends, and provide investment advice.

Web3鈥檚 uniqueness to vertical AI agents lies in its emphasis on autonomy, decentralization, and on-chain integration. Traditional AI services often rely on centralized data silos, while Web3-native AI agents achieve higher transparency and trust through on-chain verifiability. This feature gives Web3 agents an advantage in data processing and result credibility.

In the cryptocurrency field, community interaction and personalization are particularly important, so Web3 AI agents are moving towards a more personalized and interactive direction. Unlike the usually cold and functional AI agents in Web2, Web3 agents have gradually formed unique personalities and interaction patterns to adapt to the culture of decentralized communities. For example:

AI influencers: such as @aixbt_agent , share unique insights and cutting-edge market information on Crypto Twitter to attract community attention.
رمز مميز analysis agents: such as @unit 0 0x 0 , @kwantxbt , @tri_sigma_ , @mobyagent ، و @_AgentScarlett , focus on analyzing token data and providing relevant recommendations.
Research agents like @DV_Memetics و @S 4 mmyEth provide actionable market intelligence via @orbitcryptoai .
DeFAI Agent: Focused on managing LPing, Yield Farming and trading strategies, developed by teams such as @Cod3xOrg , @gizatechxyz و @autonolas .

In addition, AI model platforms such as @NousResearch , @BagelOpenAI ، و @PondGNN are further enhancing the personalization capabilities of agents to make them more tailored to the needs of decentralized communities. As DeFAI agents gradually simplify the complex operations of DeFi, they may become a key driving force in attracting billions of new users to the blockchain world. These agents are expected to set off a new wave of AI adoption in the future by lowering the threshold for using DeFi and providing users with a more intuitive experience.

4. AI Personal Assistant

AI personal assistants are revolutionizing the way we handle daily tasks, making many previously unimaginable capabilities a reality by providing convenience and automation. These assistants will no longer be limited to reminders and scheduling, but will be able to proactively make decisions and help users manage their time and resources more efficiently.

Imagine an AI that can book travel for you, recommend restaurants based on your preferences, check traffic conditions, and automatically adjust meeting schedules if you are late. It can also summarize the meeting content, make follow-up suggestions, and even automatically book transportation. In addition, it can organize your photos, sort by place and event, and generate beautiful memory albums for easy reference at any time.

With the support of Web3, these features will be further expanded:

إنزال جوي Agents: Help users scan all wallets and automatically detect whether they meet the airdrop conditions of crypto projects (such as @berachain , @monad_xyz , @StoryProtocol ).
Yield Farming LP Management Agents: Track and optimize DeFi positions in real time, automatically claim rewards and compound yields into the best strategies.
GitHub repository analysis agents: such as @soleng_agent , which can assess the strength of the project development team and help users identify potential scams.
Automated Trading Agents: such as @Cod3xOrg و @Almanak__ , execute trades based on preset conditions, optimizing the timing of entering and exiting positions to maximize market gains.

The next generation of AI personal assistants will no longer be passive assistants, but co-pilots who can take proactive actions. As AI models continue to improve their reasoning and decision-making capabilities, these agents will move from being responsive to being predictive, able to complete complex multi-step tasks with minimal user input.

Web3 plays a key role in this transformation. Decentralized AI agents are trustworthy, transparent, and censorship-resistant, ensuring that users have full control over AI-driven workflows. This capability will allow users to hand over complex financial and operational decisions to AI, revolutionizing the way we work.

5. AI App Store

AI app stores are one of the most anticipated developments in the field of artificial intelligence. Just as mobile app stores have changed the way software is distributed, AI agents also need a dedicated المتجر where users can easily discover, purchase, and integrate AI-driven applications.

In Web3, this concept is evolving into a combination of Multi-Agent Orchestration Network (MAO) and Agent Distribution Network:

Agent Distribution Network: Attract developers, investors, and users to join the ecosystem. For example, @virtuals_io is building an Agent Society where different AI agents can coexist and collaborate with each other.
MAO Network: Through intelligent matching technology, it recommends suitable AI applications to users and efficiently coordinates multiple intelligent agents to work together. Users do not need to search manually, they only need to express their needs, and the system can instantly combine solutions that meet their needs.

Therefore, the AI application store of Web3 is not just a trading market, it also needs to have functions such as planning, review and privacy protection, while supporting seamless interaction between intelligent entities. This model will completely change the way users interact with AI and lay the foundation for the future AI ecosystem.

Key players driving this field forward:

@virtuals_io : Committed to expanding its Agent Society blueprint, attracting high-quality agent teams to join, and taking the lead in developing inter-agent communication protocols to lay the foundation for agent collaboration.
@santavirtuals و @questflow : Optimizing resource allocation efficiency by improving the coordination ability between Virtuals agents.
Abstraction Layers projects, such as @orbitcryptoai و @HeyAnonai : By integrating AI agents and decentralized finance (DeFi) into efficient abstraction layers, the barrier to use is lowered, allowing more users to easily access these technologies.

Although AI orchestration is still in its early stages, it is foreseeable that seamlessly operating and profitable AI agents will open up a huge market, and Web3 is actively preparing to occupy an important position in this market.

6.B 2 A (Business-to-Agent)

AI agents are now more than just tools. They are becoming active participants in the digital economy, able to complete transactions, manage resources, and even collaborate with other agents. This trend has given rise to new infrastructure needs, and B2A (Business-to-Agent) has emerged to provide services specifically for AI agents.

Just as SaaS (Software-as-a-Service) has changed the way businesses operate, B2A will reتحديne how AI agents interact, transact, and operate in the digital economy. In the future, AI agents will need exclusive payment solutions, data access rights, computing power, and privacy protection frameworks. Currently, multiple Web3 projects are driving this transformation:

AI-commerce Payments: @Nevermined_io is developing payment solutions for intelligent agents, aiming to become the PayPal for AI agents.
Compute Management: Self-sustaining agents developed by @hyperbolic_labs that can efficiently manage their own computing resources.
Privacy Security Infrastructure: @PhalaNetwork , @OraProtocol ، و @brevis_zk are building a privacy-preserving computing layer to provide a secure and verifiable interaction environment for AI agents.
Quality Data Access: @getgrass_io , @vana , @getmasafi ، و @cookiedotfun provide structured, high-quality data sources to help AI agents train, learn, and operate efficiently.
Agent-to-Agent Communication: @virtuals_io is developing an agent-to-agent communication protocol to enable AI agents to collaborate efficiently.
Intellectual Property for AI: @StoryProtocol is developing a TCP/IP-like framework for managing the intellectual property of AI-generated content, enabling intelligent agents to autonomously manage and license the content they create.

B2A is not just a theoretical concept鈥攊t鈥檚 becoming a reality. As AI agents grow in power and complexity, they require specialized infrastructure to support their independent operations in the economic ecosystem. If you haven鈥檛 started thinking about how to serve the AI agent market, you may have missed the boat.

ملخص

AI agents are redefining the way we interact, build, and automate in Web2 and Web3. As Web3 native AI ecosystems emerge, they bring new models including open source collaboration, agent-driven business models, and decentralized automation solutions.

Although the convergence of AI and crypto is still in its early stages, its momentum is unstoppable. Web3 provides AI agents with key capabilities that Web2 cannot achieve: such as asset ownership, a permissionless innovation environment, and a highly composable ecosystem. These features create endless possibilities for an agent-driven economy. The question is no longer whether AI agents will change Web3, but how quickly this change will come and which industries will be at the heart of this change.

As the agent-driven economy continues to scale, whether you are a developer, investor, or a curious observer, now is the best time to pay attention to this field. The infrastructure is being built rapidly, key players are being formed, and opportunities are already emerging.

So, the question is: Are you ready to join this wave of change?

This article is sourced from the internet: Interpretation of Y Combinators Spring Startup Guide: Six AI Agent Tracks Layout Future Startup Trends

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