As the AI agent industry has grown, the market has shifted dramatically from agents that initially focused solely on personalization. In the early days, people were attracted to agents who could entertain, tell jokes, or “create vibes” on social media. These agents certainly generated buzz and attention, but as the market evolved, it became clear that practical value was far more important than personalization.
Many personalization-focused agents attracted huge attention when they were launched, but eventually faded away due to their inability to provide value beyond superficial interactions. This trend highlights a key lesson: In the Web3 industry, substantive value takes precedence over superficiality, and usefulness trumps novelty.
This evolution mirrors the shift in the Web2 AI industry. Specialized large language models (LLM) are being continuously developed to respond to the specific needs of niche industries such as finance, law, and real estate. These models focus more on accuracy and reliability, making up for the shortcomings of general-purpose AI.
The limitation of general AI is that it often can only provide "almost" answers, which is unacceptable in some scenarios. For example, a popular model may only be 70% accurate on a specific specialized problem. This may be sufficient for day-to-day use, but could have disastrous consequences in high-stakes scenarios involving court judgments or major financial decisions. This is why professional LLMs that are finely tuned to achieve 98% to 99% accuracy are becoming increasingly important.
So the question is: why choose Web3? Why not let Web2 dominate the professional AI industry?
Web3 has several significant advantages over traditional Web2 AI:
The first is global liquidity. Web3 allows teams to access funding more efficiently. Through token issuance, AI projects can directly access global liquidity and avoid time-consuming VC meetings and negotiations. This approach democratizes financing and allows developers to obtain the resources they need faster.
The second is to achieve value accumulation through token economics. Tokens enable the team to reward early adopters, incentivize holders, and maintain the sustainable development of the ecosystem. For example, Virtuals allocates 1% of transaction fees to cover inference costs, ensuring that its agents remain functional and competitive without relying on external funding.
The third is decentralized AI infrastructure. Web3 offers open source models, decentralized computing resources such as Hyperbolic and Aethir, and massive open data pipelines such as Cookie DAO and Vana, giving developers a collaborative and cost-effective platform that is difficult to replicate in Web2. More importantly, it fosters a passionate community of developers who work together to drive innovation.
Web3 AI Ecosystem
In the Web3 AI agent ecosystem, we see that each ecosystem improves its capabilities by integrating new functions and opens up new application scenarios. From the Bittensor subnet to Olas, Pond, and Flock, these ecosystems are building more interoperable and functional agents. At the same time, easy-to-use tools like SendAI’s Solana Agent Kit or the Coinbase CDP SDK are also emerging.
The following ecosystems are building utility-first AI applications:
ALCHEMIST AI has developed a codeless AI application building platform.
MyShell has created an AI application store focusing on image generation, visual novels and virtual character simulation.
Source: PANews
Questflow launched the Multi-Agent Orchestration Protocol (MAOP), dedicated to productivity-enhancing application scenarios, and its integration with Virtuals created a Santa agent for gamified airdrops and incentive management.
Source: PANews
Capx AI has launched a practical-first AI app store on Telegram.
Source: PANews
Individual agents focused on practical applications
Outside the ecosystem, individual agents in professional industries are also emerging. For example:
Corporate Audit AI acts as a financial analysis AI agent that reviews reports and identifies market opportunities.
Source: PANews
$CPAAgent was developed by Tj Dunham and focuses on calculating cryptocurrency taxes and generating reports for users.
Source: PANews
This shift from “chatbots chatting away on social media” to “experts sharing professional insights” is here to stay.
The future of AI agents
The future of AI agents lies not in chatbots making casual small talk, but in expert agents across specialized industries delivering value and insights in engaging ways. These agents will continue to create mindshare and direct users to actual products, whether it's a trading terminal, a tax calculator or a productivity tool.
Where will the value be concentrated?
The biggest beneficiaries will be the agentive L1 and coordination layers.
When it comes to proxy L1, platforms like Virtuals and ai16z are raising the bar in the industry and ensuring their ecosystems prioritize quality. Virtuals remains the top L1 platform in the agency industry, and ai16z’s launch platform will soon join the competition. Purely personalized agents are disappearing, replaced by agents that are both practical and engaging.
In terms of the coordination layer, platforms like Theoriq will orchestrate the collaboration of a large number of agents, integrating their strengths to provide users with seamless and powerful solutions. Imagine bringing together agents like aixbt, gekko, and CPA to capture alpha, execute trades, and handle taxes in one unified workflow. Theoriq’s task-based discovery architecture is moving toward unlocking this collective intelligence.
Source: PANews
final thoughts
The narrative of AI applications that prioritizes utility has only just begun. Web3 has a unique opportunity to carve out a world where AI agents can not only entertain, but solve real problems, automate complex tasks, and create value for users. 2025 will see a shift from chatbots to collaborative assistants, with specialized LLM and multi-agent orchestration redefining the perception of AI.
While Web2 and Web3 will gradually converge, the open, collaborative nature of Web3 will lay the foundation for the most innovative breakthroughs. It’s no longer about “AI agents with personalities” but about agents that provide practical value and create meaningful impact. Of note are agentive L1s, coordination layers, and emerging AI applications. The age of agency has arrived, and this is just the beginning.
This article is reproduced with permission from: (PANews)
Original author: 0xJeff
"From chat to real application!" Introduction to 6 Web3+AI practical products, the era of AI agents has arrived." This article was first published in "CryptoCity"