Article author: Stepan Gershuni Article compilation: Block unicorn

OpenAI, Google, Microsoft, and Coinbase, NEAR The founder of EigenLayer and EigenLayer both said that agents are the future of AI. This will be the most important technology wave of this decade for the Web2 and Web3 world. Let’s explore what opportunities are created by on-chain proxies and why have they reached a market cap of nearly $4 billion in just one month? What’s next for Web3 proxies beyond the hype and speculation?

Here is my discussion of Web3 AI agents: What are they? How did they reach a $3 billion market cap in less than a month? How do they work? And what opportunities exist?

Market cycle of Web3

AI agents are gradually and steadily realizing most knowledge Automate tasks and drive efficiency: from entertainment and social media to business productivity, marketing, finance, investing, healthcare and education.

Blockchain and Web3 provide unique capabilities for AI agents, enabling trustless composability, verifiability, and programmable contracts, for the agency economy.

Unlike memecoins, NFTs and ICOs, AI proxy tokens can provide utility and mark a shift to a more substantial value proposition. Market narratives will ebb and flow, but the real product will always be there. Specifically, Web3 agents differ from existing AI applications in two main characteristics:

1. Autonomy: The agent operates in a decentralized computing environment

2. Economy: For users and investors, participating in the economic activities of the agent is more transparent and simple because it is based on the blockchain , programmable and public.

Currently, we are in a phase similar to the early experiments with Ethereum in 2016. But by the middle or late next year, we may see agents handling complex tasks, automating processes, and creating significantcash flow. Replacing human jobs with robots is inevitable—but at least you can own tokens of them.

The current situation of Web3 proxy

Currently, most Web3 AI agents use large language models (LLM) as the core to generate text-based content through platforms such as Twitter and Discord. These agents are often designed to emulate a character or personality and engage users primarily through social media interactions.

Main functions include:

Transaction: Agents are usually associated with a certain token or currency Related, users can trade freely on various chains or centralized exchanges.

Content generation: Generate text replies, tweets, or messages based on preset prompts or fine-tuned models.

Role playing: Interacting with users as a specific character or entity.

Community participation: Allow users to influence agent behavior through prompts or feedback and enhance their sense of participation.

The current economic model of Web3 AI agents revolves around tokens associated with each agent. These tokens have the following characteristics:

Speculative assets: Mainly traded on exchanges, their value is driven more by market speculation than actual use value.

Lack of trustless integration: The token is not intrinsically tied to the operation of the agent in a decentralized or verifiable manner.

Lack of cash flow: Most agents fail to generate revenue for token holders, limiting the long-term value of the token.

Although blockchain promotes the concept of decentralization, most current AI agents still face centralization problems:

Centralized reasoning: Rely on centralized services such as Runpod (cloud computing platform) or Lambda (computing service platform) for model reasoning.

Server dependency: The agent's behavior, reasoning, and memory are managed by a central server, which creates the risk of a single point of failure. The agent is usually a Python script running on a single machine, using Langchain, CrewAI, or similar libraries.

Limited transparency: Centralized control of proxy operations reduces transparency and trust between users and token holders, which may lead to project parties running away after attracting investment. , or the agent creator makes decisions without authorization. style="text-align:center">

Web3 AI agent design space

The design space of Web3 AI agent is broad, providing a variety of possibilities

Entertainment ↔ Practical Range

Developers can position their AI agents on a spectrum ranging from pure entertainment to practical utility, with different positions attracting different market audiences.

Entertainment-focused agents

These agents prioritize engaging users through fun and interactive experiences and may:

Role playing: Taking on a dynamic role in a social media or virtual environment and engaging users through storytelling or humor.

Hosting media content: running a podcast, Live or virtual events, engage your audience with unique AI-generated content

Community engagement: Encourage user engagement by letting the community influence agent behavior or narratives

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Utility-focused agency

These agents are designed to solve real-world problems and provide tangible value by automating tasks and increasing productivity, and they may:

Automated knowledge work: Perform tasks such as coding, data analysis, market research, or content creation

Provide services: Act as an AI counselor, educator, customer support. or social media manager.

Improve business operations: optimize workflows, improve decision-making, or provide insights through advanced analytics.

Agency ↔ Online time

There is a trade-off between developing highly autonomous agents with advanced capabilities and getting products to market quickly.

Quick and easy deployment:

Quick rollout: can leverage existing models and centralized infrastructure Facility to quickly develop and deploy simple agents.

Limited functionality: These agents may only provide basic functionality and rely on a single interaction method, such as text interaction.

Speculative appeal: By catering to current trends, they quickly attract attention and investment, often driven by speculative trading.

Complex and time-consuming development:

On-chain governance: achieving decentralized control Mechanism that allows the community to govern the parameters and development of the agent.

Share revenue with token holders: Build an economic model that allows agents to generate revenue and benefit token holders.

Community-owned agents: Create agents that are jointly owned and managed by the community to enhance participation and sustainability.

Infrastructure requirements: Developing these capabilities requires building robust frameworks, decentralized reasoning systems, and privacy-preserving computing, which requires time and resources.

MEME Coin ↔ Actual Cash Flow

The economic model of AI agent tokens can start from speculation Ranging from sexual assets to tokens supported by actual economic value, the value of speculative MEME coins is mainly obtained through market speculation and speculative transactions, and usually has no intrinsic use value. Although this approach is risky, it serves as a launch mechanism to help new projects attract initial investment and community attention. However, it is important to note that relying solely on speculation mayLeading to market volatility and potential losses, thereby undermining the long-term sustainability of the project.

However, reliance on speculation leads to market volatility and potential losses, undermining long-term sustainability.

Tokens with real cash flows provide tangible value to holders and promote a more sustainable ecosystem. These tokens represent rights to access agent services, governance rights, or revenue sharing, creating a direct link between agent performance and token value. This docking promotes a stable and growing ecosystem, as the value of the token is closely tied to the utility and success of the agent. In addition, providing real financial benefits can enhance investor confidence and encourage long-term participation and support for projects. Not only does this approach attract serious investors, it also helps build a more resilient and value-driven token economy in the AI ​​agent space.

The future of Web3 AI agents

I think it is likely that AI agents will be available in 2025 Becoming a dominant crypto topic. However, there are still many unanswered questions and room for growth. Below, I will try to structure possible areas of improvement for the AI ​​agent to make it more useful and better.

Better token economic model

Developing robust and sustainable economic models is critical to the long-term success of AI agents.

Revenue sharing is a key aspect of AI agent token economics. Agents can generate revenue through services such as content creation, coding or analysis and distribute profits to token holders. This aligns financial rewards with agent performance, incentivizing community members to contribute to the agent’s success.

Value based on utility creates tangible benefits for token holders. Access tokens give users permission to use agent services, while governance rights allow token holders to influence development decisions, agent parameters and strategic direction.

Community ownership empowers users through decentralized control. Implementing a governance mechanism enables the community to co-manage the agent. Multi-signature wallets and smart contracts provide a secure and transparent way to handle payments, income distribution, and agent behavior.

Mechanism design helps to establish the relationship between agentsvirtuous behavior, creating an agent economy in which agents can autonomously conduct transactions, negotiate and reach agreements. Smart contracts play a vital role in automating transactions and executing agreements without the need for intermediaries.

Agent collaboration focuses on coordinating multiple agents to work together to improve the overall capabilities and efficiency of the system.

True decentralization

Overcoming the challenges of centralization is critical to building trust and securing AI agents Flexibility is crucial.

Reasoning and execution are key aspects of decentralized AI agents. Decentralized inference spreads model processing across the network or edge devices, eliminating dependence on central servers. Privacy-preserving computing ensures the security of data and computing through methods such as zero-knowledge proofs. These methods enhance the system's ability to withstand outages and audits while ensuring verifiable execution for community auditing.

On-chain execution moves the agent's reasoning and decision-making process onto the blockchain, significantly increasing transparency and trustlessness. This involves implementing smart contract logic for agent operations. Additionally, leveraging edge computing and distributed systems enables efficient and secure computing in decentralized networks.

Community governance plays a vital role in truly decentralized AI agents. By empowering token holders with collective decision-making capabilities, the risk of centralized control is reduced. Governance mechanisms ensure that agents’ behavior is consistent with the community’s values ​​and goals, creating a more democratic and user-driven development process.

More interaction methods and better development tools

Expanding the capabilities of AI agents and improving development tools will drive their utility and foster adoption in the wider market.

Multimodal interaction is crucial, combining voice, video and other communication methods to enrich the user experience. Advanced communication methods enable agents to exchange machine-readable data, such as embeddings or model parameters, making interactions between agents more efficient.

Access to specialized models, such as time series analysis, can open more usage scenarios for Web3 agents. For example, they can become traders and DeFi strategy optimizers, representing usersAccount management and allocation of capital.

Tool advancement is crucial to the growth of AI agents. A powerful framework simplifies the creation of complex functionality and management of agents. Integrate advanced logic, including reasoning, planning, self-criticism, and tool integration, to enable agents to perform complex tasks. Establishing interoperability standards and protocols promotes seamless multi-agent collaboration, paving the way for more complex and efficient AI ecosystems.

Workflow orchestration system plays a key role in optimizing AI agent performance. These systems are able to adjust workflows in real time, dynamically adapting based on performance and changing conditions. They can also facilitate the selection of optimal paths, balancing cost and quality in completing tasks. Encouraging interactions between agents can lead to emergent behavior, promoting innovative solutions and capabilities. Additionally, designing fault-tolerant distributed systems ensures that even if some agents fail, the system continues to operate effectively.

Security is always the primary issue in AI agent development. Addressing AI-specific security challenges, such as model theft, hint injection, and data poisoning attacks, is critical to building trust and reliability. It is also important to ensure auditability and explainability, making the agent’s decision-making process transparent and auditable to ensure compliance and trust. These measures are critical for the widespread adoption and integration of AI agents in various fields.

Proxy experience

Like many aspects of the crypto industry, using a Web3 proxy is not easy , often requiring complex knowledge. However, this is about to change.

Web3 proxy will significantly improve the user experience, adding dynamically generated interfaces that adapt to each user's personalized preferences. Agents will generate UI elements in real time, using familiar and convenient UI patterns to tailor the interface for each user.

Voice commands, 3D avatars, and augmented reality functions enhance the interactive experience and make it more natural. Connect seamlessly and even integrate within crypto wallets and other tools for frictionless interactions. Persistent memory systems allow for more personalized and relevant interactions.

7/24-hour availability, automatic tasks can be performed after authorization, such as monitoring token prices, executing transactions at specific thresholds, regularly publishing social media content, or based on Automatically reply to Discord messages with preset trigger conditions. Agents can also proactively notify users about important events, such as governance proposals and upcoming tokens.or suspicious wallet activity that requires attention.

This shift will elevate agents from basic chatbots to truly useful agents capable of entertaining users or helping solve problems at scale.

Agents will replace human labor

I believe AI agents represent the fundamentals of the global workforce change. Over time, more and more jobs will be automated, and AI agents will complete tasks faster, cheaper, and more efficiently—ultimately leading to greater prosperity. The combination of AI agency and tokenization provides users with the opportunity to own a share of the post-labor economy, ensuring automated revenue distribution rather than concentration in the hands of a few tech giants.

While today's Web3 proxies are very similar to their speculative counterparts, the MEME coin, I expect they will gradually develop real utility and real value.

The cyber economy is coming, and groups are awakening.