This article discusses the future of AI agents in Web3, depicting a transition from "entertaining chatbots" to "professional problem solvers". It emphasizes the unique advantages of Web3, such as global liquidity, decentralization, and token economics, which make AI agents not only more practical but also able to create profound value for users. The article looks ahead to 2025, when specialized large language models and multi-agent collaboration will redefine the role of AI, and the fusion of Web2 and Web3 will drive innovation, ushering in an era of AI centered on practicality.
Author: Defi0xJeff, Crypto Kol
Source: X Platform
Translator: zhouzhou, BlockBeats
Since the early stages of AI agents, agent-focused agents have come a long way. Initially, we were drawn to agents that could entertain us, crack jokes, or simply "tease" us. These agents captured people's attention and generated a lot of hype, but as the market has evolved, one thing has become increasingly clear: value and practicality are more important than personality.
We have seen countless personality-driven agents launched, and although they initially generated a lot of heat, they have gradually faded from view as they failed to provide content beyond surface-level interactions. This trend highlights an important lesson - Web3 values substance over style, and practicality over novelty.
This evolution echoes a similar transition in AI within Web2. Increasingly, specialized large language models (LLMs) are being developed, optimized for specific use cases, from finance to law, real estate, and beyond. These models focus on accuracy and reliability, filling the gaps left by general-purpose AI.
The challenge with general AI is that it often provides "good enough" answers, but in many cases, this is not acceptable. For example, a popular model may only have a 70% accuracy rate in a particular niche, which may be passable for everyday use, but in high-risk scenarios - such as whether you can win a lawsuit or whether you will lose millions in a financial decision - this becomes very problematic. This is why specialized LLMs, fine-tuned to achieve 98%-99% accuracy, are becoming increasingly important.
The key question we need to explore next is: Why Web3? Why not let Web2 dominate the realm of specialized AI?
Web3 offers several advantages that Web2 struggles to match:
Global Liquidity
Web3 enables teams to raise capital more efficiently. Through token issuance, an AI project can quickly access global liquid funds, bypassing months of venture capital meetings and negotiations. This democratizes access to funding, allowing developers to obtain resources for development much faster.
Token Economics Facilitates Value Accrual
Tokens allow teams to reward early adopters, incentivize holders, and sustain the ecosystem's operations. For example, virtuals.io allocates 1% of transaction fees to pay for inference costs, ensuring their agents remain functional and competitive without relying on external funding.
DeAI Infrastructure
Web3 provides open-source models, decentralized computing (such as Hyperbolic Labs and AethirCloud), and access to vast open data pipelines (like Cookie.fun and Withvana), making it possible to build a collaborative and cost-effective infrastructure that is difficult to replicate in Web2.
More importantly, Web3 fosters a passionate developer community driving innovation.
Web3 AI Ecosystem
Within the Web3 AI agent ecosystem, we are starting to see the ecosystem enhance capabilities and unlock new use cases through integrated technologies. From Bittensor subnets to Olas, Pond, and Flock, the ecosystem is building more interoperable and powerful agents. Meanwhile, increasingly user-friendly tools are emerging to augment functionality, such as Solana Agent Kit by SendAI.fun or the CDP SDK by Coinbase, building an ecosystem of practicality-first AI applications.
AlchemistAIApp: A no-code AI application building platform.
MyShellAI: An AI app store focused on image generation, visual novels, and "wife simulators".
Questflow: A multi-agent coordination protocol (MAOP) for productivity-enhancing use cases. Questflow's integration with Virtuals created the Santa agent, which gamifies airdrops and manages incentive mechanisms.
0xCapx: A practicality-focused AI app store, located on Telegram.
In addition to the ecosystem, individual agents focused on real-world use cases are also starting to emerge:
CorpAuditAI: A financial analysis AI agent responsible for reviewing reports and identifying market opportunities.
CPA Agent: Built by RealTjDunham, this agent calculates crypto taxes and generates reports for users.
This transition from "chatbots that ramble on CT" to "domain experts sharing insights" will continue. The future of AI agents is not random chatbots, but domain-specific experts providing value and insights in engaging ways. These agents will continue to capture user attention and funnel them into actual products - whether trading terminals, tax calculators, or productivity tools.
Where Will the Value Flow?
The biggest beneficiaries will be Agentic L1s and Coordination Layers.
· Agentic L1s: Platforms like Virtuals.io and AI16zDAO are raising the bar, ensuring their ecosystems focus on quality. Virtuals remains the top L1 for agents, and soon, AI16zDAO's launchpad will join the competition. Agents relying solely on personality are disappearing, replaced by those that are both useful and engaging.
· Coordination Layers: These layers, like TheoriqAI, will coordinate a group of agents, combining their strengths to provide seamless and powerful results for users. Imagine integrating agents like aixbt, Gekko, and CPA to provide alpha sources, execute trades, and handle taxes - all within a cohesive workflow. Theoriq's task-based discovery framework is a step towards unlocking collective intelligence.
Final Thoughts
The narrative of practicality-first AI applications is just beginning. Web3 has a unique opportunity to carve out a space where AI agents are not just for entertainment, but for solving real problems, automating complex tasks, and creating value for users.
2025 will be the year of the transition from chatbots to co-pilots, as specialized large language models (LLMs) and multi-agent coordination redefine our perception of AI. Web2 and Web3 will converge, but Web3's open, collaborative nature will lay the foundation for some of the most innovative breakthroughs.
No longer "AI agents with personality", but those that provide practicality and create meaningful impact. Keep an eye on Agentic L1s, Coordination Layers, and the emerging AI applications. The age of Agentic is upon us - and this is just the beginning.
