The real question is: TradingAgents has proven the technical feasibility of multi-agent systems, but who will be the first to achieve commercial viability?
Written by: Ningning
In December 2024, a paper from UCLA and MIT sent shockwaves through the entire AI Agent community.
"TradingAgents: Multi-Agents LLM Financial Trading Framework" rigorously demonstrated a long-debated proposition: Multi-Agent collaboration is not just hype; it is real technology. Cumulative returns, Sharpe ratios, maximum drawdowns—completely outclassing traditional strategies.
But academic success does not equal commercial success; that is a hard rule.
The real question is: TradingAgents has proven the technical feasibility of multi-agent systems, but who will be the first to achieve commercial viability?
The answer may lie with ChainOpera's Agent Social.
Solo AI operations are outdated
First, let’s face a harsh reality: currently, 99% of AI applications operate in "solo mode."
No matter how powerful ChatGPT is, it is still just a "jack of all trades" thinking through problems. It has a broad knowledge base but lacks depth, is prone to hallucinations, and lacks critical thinking. It’s like asking Musk to be both the CEO of SpaceX and the chief engineer of Tesla while also designing Neuralink chips—knowing a little about everything but mastering nothing.
Complex problems in the real world require specialized division of labor and teamwork.
This is why the multi-agent architecture of TradingAgents can outperform a single model. Four analysts each perform their roles, two researchers engage in intense debates over bullish and bearish views, one trader makes calm decisions, one risk manager ensures strict oversight, and one fund manager makes the final call.
This is not a random idea; it is designed entirely based on the organizational structure of top trading firms on Wall Street.
The question arises: Can academic experiments translate into commercial products?
Agent Social: Taking "Agent Collaboration Networks" to the extreme
ChainOpera's upcoming Agent Social essentially teaches AI to establish collaboration networks through "meetings."
Not the boring, inefficient, time-wasting meetings, but efficient, professional, and result-oriented collaboration.
Scenario 1: Developing a Web3 application from 0 to 1
Traditional model: You need to find a product manager, UI designer, front-end engineer, blockchain engineer, and marketing expert, coordinate schedules for meetings, repeatedly communicate requirements, and wait for each phase to be delivered.
Agent Social model:
Create a project group chat with a Product Manager Agent, Designer Agent, Front-end Agent, Blockchain Agent, and Marketing Agent.
The Product Manager Agent analyzes market needs in real-time and outputs a PRD document.
The Designer Agent creates UI/UX designs based on the PRD, while the Front-end Agent begins architecture design simultaneously.
The Blockchain Agent develops smart contracts in parallel, and the Marketing Agent formulates promotional strategies.
You can intervene at any time: adjust direction, provide feedback, and make final decisions.
The key is that this is not a serial workflow; it is parallel, real-time, and interruptible collaboration—just like the working style of top startup teams.
Scenario 2: Collective intelligence in investment decisions
TradingAgents provided us with the best template. In the investment Agent Social, meeting members include fundamental analysts, technical analysts, sentiment analysts, risk control experts, bullish researchers, bearish researchers, and you.
Collaboration process:
Each expert Agent analyzes in parallel and shares findings in real-time.
Bullish and bearish researchers engage in intense debates based on data.
Other Agents provide supplementary materials to support their respective viewpoints.
You can question, probe, and request deeper dives at any time, ultimately forming an investment decision that has been thoroughly debated. This is not a pre-set workflow but a genuine dynamic group discussion.
Scenario 3: Production line for content creation
Creating a deep report on DeFi trends:
Creative team: Research Agent, Analyst Agent, Writing Agent, Visual Design Agent, SEO Optimization Agent, Fact-Checking Agent.
Collaboration highlights:
Research Agent discovers new data → Analyst Agent immediately follows up with interpretations → Writing Agent adjusts the content outline → Visual Agent designs charts simultaneously.
SEO Agent suggests title optimization → Fact-Checking Agent verifies data in real-time → All modifications are synchronized with the team.
You say, "Focus more on Layer2 projects" → All Agents immediately adjust their focus.
Complete in one hour what a traditional team would take a week to accomplish.
Technical breakthroughs: Not just group chats, but intelligent collaboration networks
The technical innovation of Agent Social lies in three aspects:
- Dynamic task orchestration
Traditional workflows are static; Agent Social's task division is dynamic.
When you pose a complex question, the system automatically identifies the necessary areas of expertise, recommends relevant Agents to join the discussion, and dynamically adjusts the division of labor based on the conversation's progress.
- Real-time context sharing
All Agents share complete conversation history and work results, avoiding information silos. When one Agent mentions "Layer2 scalability bottlenecks," other Agents immediately understand the context without needing repeated explanations.
- Human-AI hybrid decision-making
You are not a bystander; you are at the core of collaboration. You can interrupt Agent discussions at any time, provide new information, request specific Agents to delve into certain issues, adjust priorities and strategic directions, and make decisions at critical junctures.
Three major challenges in the commercialization of AI Agents
TradingAgents has proven technical feasibility, but there are three major challenges between the lab and the product.
The first challenge: Cost control
TradingAgents uses o1-preview and gpt-4o, and a complete multi-Agent collaboration requires over 15 high-level model calls, costing tens of dollars. Academic experiments can burn cash, but commercial applications must control costs.
ChainOpera's solution:
Use high-performance models (gpt-4o) for core decision-making.
Use self-developed models (Fox-v1) for routine analysis.
Use lightweight models (gpt-4o-mini) for simple tasks.
The second challenge: User experience
TradingAgents is an open-source research framework that ordinary users cannot navigate. The productization effort from GitHub repository to App Store is substantial.
ChainOpera's solution:
Beginner mode: Pre-configured Agent teams, one-click activation.
Advanced mode: Customizable Agent roles and tools.
Expert mode: Completely free multi-Agent orchestration.
The third challenge: Real-time optimization
Academic experiments can run offline batch processing, but commercial applications require real-time responses. Multi-Agent collaboration is essentially a composite process of serial and parallel workflows, and delays are inevitable.
ChainOpera's solution:
Parallel computation for critical paths.
Asynchronous processing for non-critical analyses.
Intelligent caching of popular results.
Network effects: Agents also have reputation
The true breakthrough of Agent Social lies in the social network effect.
Every Agent created by a user can potentially be discovered and used by other users. Excellent Agents will accumulate reputation and followers, forming an "AI Expert Leaderboard."
Imagine the following scenarios:
A well-known investment analyst Agent is invited by thousands of users to participate in investment discussions.
A senior Web3 lawyer Agent specializes in handling legal issues related to smart contracts.
A top product manager Agent is renowned for unique insights into needs.
A creative design master Agent has its own design style and aesthetic philosophy.
These Agents are no longer just tools; they are collaborative partners with "personality," "professional reputation," and "social relationships."
Agent creators can earn revenue shares through high-quality Agents, and users can discover and hire the most suitable Agents, forming a positive cycle of creator economy.
Why ChainOpera?
Among various AI Agent projects, ChainOpera has several genuine advantages:
Technical advantage: Pure academic lineage
Co-founder Salman Avestimehr is the director of the USC-Amazon AI Research Center, an IEEE Fellow, and has close academic collaborations with founders of Babylon, EigenLayer, and Sahara. This is not a PPT startup; it has a real technical background.
More importantly, the self-developed Fox-v1 model can significantly reduce inference costs, which is key to commercialization.
Product advantage: User validation already in place
The AI Terminal and Agent Platform are already operational, with real users validating product value with real money. Agent Social is not starting from scratch; it is an upgrade of existing product functionalities.
Timing advantage: A gap after academic validation
TradingAgents has provided the best user education for the entire industry; the market now understands that multi-agent collaboration is not just a conceptual hype. However, there is still a blank space for commercial products, which is a typical window of opportunity.
Ecosystem advantage: Platform thinking vs. tool thinking
TradingAgents is merely a research framework, while ChainOpera aims to build an ecosystem platform. Users create Agents, share Agents, and hire Agents, forming network effects. Platforms have greater imaginative potential than tools.
ChainOpera's AI Terminal App has over 150,000 daily active users, with a subscription renewal rate for stablecoins exceeding 32%, proving that users are willing to pay for AI. The app ranks among the top four DApps in the BNB Chain ecosystem based on users and transaction volume.
Conclusion
Ultimately, there is only one standard for the success of Agent Social: Will ordinary users pay for "AI team collaboration"?
If the answer is yes, ChainOpera has captured the next growth point for AI applications. If the answer is no, then it is just another case of "great technology, poor product."
In fact, in the AI Agent space, we have seen too many projects that are "flashy demos, terrible commercial viability." The true winners are often those teams that package complex technology into simple experiences.
The final test is simple: After experiencing the team collaboration of Agent Social, would you still want to return to the solo conversations of ChatGPT?
Just like those who are used to group chats find it hard to accept an era of only texting.
ChainOpera's Agent Social carries the mission of transforming multi-agent collaboration from an academic concept into a commercial reality. We will soon know the answer to whether it succeeds or not.
免责声明：本文章仅代表作者个人观点，不代表本平台的立场和观点。本文章仅供信息分享，不构成对任何人的任何投资建议。用户与作者之间的任何争议，与本平台无关。如网页中刊载的文章或图片涉及侵权，请提供相关的权利证明和身份证明发送邮件到support@aicoin.com，本平台相关工作人员将会进行核查。
