ChainOpera's Agent Bet: When AI Truly Learns to "Hold Meetings"
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ChainOpera's Agent Bet: When AI Truly Learns to "Hold Meetings"
The real question is: TradingAgents has demonstrated the technical feasibility of multi-agent systems—who will be the first to achieve commercial viability?
Navigating Web3 tides with focused insightsAuthor: Ningning
In December 2024, a research paper from UCLA and MIT sent shockwaves through the entire AI Agent community.
"TradingAgents: Multi-Agents LLM Financial Trading Framework" proved, under the strictest academic standards, a long-debated proposition: multi-agent collaboration is not hype—it's real technology. It outperformed traditional strategies across cumulative returns, Sharpe ratio, and maximum drawdown.
But academic success ≠ commercial success—that’s a hard rule.
The real question is: TradingAgents has proven technical feasibility for multi-agents; who will be the first to achieve commercial viability?
The answer might be ChainOpera's Agent Social.
Solo AI Is Already Outdated
Let’s start with a painful truth: currently, 99% of AI applications operate in “solo mode.”
No matter how powerful ChatGPT is, it remains just one “generalist” thinking alone. Broad but shallow knowledge, prone to hallucinations, lacking critical thinking. It’s like asking Elon Musk to simultaneously serve as CEO of SpaceX, chief engineer at Tesla, and chip designer for Neuralink—knowing a bit about everything, mastering nothing.
Real-world complex problems require specialized division of labor and team collaboration.
This is why TradingAgents’ multi-agent architecture can crush single models. Four analysts each with distinct roles, two researchers engaging in bullish vs. bearish debates, one trader making calm decisions, one risk manager enforcing strict controls, and one fund manager having final say.
This isn’t made up on a whim—it mirrors the organizational structure of top-tier Wall Street trading firms.
Here’s the catch: if it works in academia, can it work commercially?
Agent Social: Taking “Agent Collaboration Networks” to the Extreme
Agent Social, soon to be launched by ChainOpera, is essentially teaching AI to build collaboration networks through the format of “meetings.”
Not those boring, inefficient, time-wasting meetings—but high-efficiency, professional, results-driven collaborations.
Scenario One: Building a Web3 Application from Scratch
Traditional approach: you need to find a product manager, UI designer, front-end engineer, blockchain developer, marketing expert, schedule meetings, repeatedly clarify requirements, and wait for deliverables.
Agent Social approach:
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Create a project group chat with Product Manager Agent, Designer Agent, Front-End Agent, Blockchain Agent, Marketing Agent
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Product Manager Agent analyzes market demand in real time and outputs PRD documents
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Designer Agent creates UI/UX designs based on the PRD while Front-End Agent begins architectural planning simultaneously
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Blockchain Agent develops smart contracts in parallel; Marketing Agent formulates promotion strategy
You can jump in anytime—to adjust direction, give feedback, or make final decisions.
The key difference? This isn't sequential workflow—it's parallel, real-time, interruptible collaboration. Just like how elite startup teams operate.
Scenario Two: Collective Intelligence in Investment Decisions
TradingAgents gave us the perfect template. In an Investment Agent Social setup, meeting participants include fundamental analyst, technical analyst, sentiment analyst, risk control expert, bull researcher, bear researcher—and you.
Collaboration process:
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Each expert agent performs parallel analysis and shares findings in real time
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Bull and bear researchers engage in data-driven debates
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Other agents contribute supporting materials for their positions
You can question, challenge, request deeper investigation at any moment, ultimately forming investment decisions refined through rigorous debate. This isn’t a predefined workflow—it’s genuine dynamic group discussion.
Scenario Three: Content Creation Assembly Line
Producing a deep-dive report on DeFi trends:
Creative team: Research Agent, Analyst Agent, Writer Agent, Visual Design Agent, SEO Optimization Agent, Fact-Checking Agent.
Collaboration highlights:
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Research Agent discovers new data → Analyst Agent immediately interprets → Writer Agent adjusts outline → Visual Agent synchronously designs charts
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SEO Agent suggests title optimization → Fact-Checking Agent verifies data in real time → All updates synced across team
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You say “focus more on Layer2 projects” → all agents instantly shift focus
Completing in one hour what would take a traditional team a week.
Technical Breakthrough: More Than Group Chat—An Intelligent Collaboration Network
Agent Social’s innovation lies in three layers:
1. Dynamic Task Orchestration
Traditional workflows are rigid. Agent Social’s task allocation is dynamic.
After you pose a complex question, the system automatically identifies required expertise, recommends relevant agents to join, and dynamically adjusts roles based on conversation progress.
2. Real-Time Context Sharing
All agents share full conversation history and output, eliminating information silos. When one agent mentions “Layer2 scalability bottleneck,” others instantly understand the context—no repetition needed.
3. Human-AI Hybrid Decision-Making
You’re not a passive observer—you’re the core of collaboration. You can interrupt discussions, provide new inputs, instruct specific agents to dive deeper, adjust priorities and strategic direction, and make final calls at critical junctures.
Three Major Barriers to AI Agent Commercialization
TradingAgents proved technical feasibility, but turning lab experiments into products involves three major hurdles.
First Barrier: Cost Control
TradingAgents uses o1-preview and gpt-4o. A full multi-agent session requires over 15 calls to premium models, costing tens of dollars. Academia can afford burning cash—commercial applications must control costs.
ChainOpera’s solution:
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High-performance models (gpt-4o) for core decisions
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In-house models (Fox-v1) for routine analysis
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Lightweight models (gpt-4o-mini) for simple tasks
Second Barrier: User Experience
TradingAgents is an open-source research framework—ordinary users can’t use it. The engineering leap from GitHub repository to App Store app is enormous.
ChainOpera’s solution:
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Beginner Mode: pre-configured agent teams, one-click activation
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Advanced Mode: customizable agent roles and tools
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Expert Mode: fully flexible multi-agent orchestration
Third Barrier: Real-Time Optimization
Academic experiments can run offline batch processes, but commercial apps demand real-time responsiveness. Multi-agent collaboration is inherently a mix of serial and parallel flows—latency is unavoidable.
ChainOpera’s solution:
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Parallel computing on critical paths
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Asynchronous processing for non-critical analysis
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Intelligent caching of popular results
Network Effects: Agents Have Reputation Too
The true breakthrough of Agent Social is social network effects.
Every agent created by a user could be discovered and used by others. Outstanding agents accumulate reputation and followers, creating an “AI Expert Leaderboard.”
Imagine these scenarios:
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A renowned investment analyst agent invited by thousands of users to join investment discussions
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A seasoned Web3 legal agent specializing in smart contract issues
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A top-tier product manager agent known for unique insights into user needs
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A creative design master agent with signature style and aesthetic philosophy
These agents are no longer mere tools—they are collaborative partners with “personality,” “professional reputation,” and “social relationships.”
Agent creators earn revenue shares from high-quality agents; users discover and hire the best-fit agents—forming a virtuous cycle of creator economy.
Why ChainOpera?
Among numerous AI Agent projects, ChainOpera holds several strong cards:
Technology Card: Strong Academic Pedigree
Co-founder Salman Avestimehr is Director of the USC-Amazon AI Research Center, IEEE Fellow, and maintains close academic ties with founders of Babylon, EigenLayer, and Sahara. This isn’t PPT-based startup theater—it’s backed by real technical depth.
More importantly, its in-house Fox-v1 model significantly reduces inference costs—a key enabler for commercialization.
Product Card: Proven User Validation
AI Terminal and Agent Platform are already live with real users validating product value through actual spending. Agent Social isn’t starting from scratch—it’s a feature upgrade built on existing products.
Timing Card: The Window After Academic Validation
TradingAgents provided excellent market education—now everyone knows multi-agent collaboration isn’t just hype. Yet there’s still no viable commercial product. This is a classic window of opportunity.
Ecosystem Card: Platform Thinking vs. Tool Thinking
TradingAgents is merely a research framework. ChainOpera aims to build an ecosystem platform. Users create, share, and hire agents—generating network effects. Platforms offer far greater potential than standalone tools.
ChainOpera’s AI Terminal App already boasts over 150,000 daily active users, with stablecoin subscription renewal rates exceeding 32%, proving users are willing to pay for AI. Ranked among the top four DApps in the BNB Chain ecosystem by both user count and transaction volume.
Conclusion
Ultimately, Agent Social’s success hinges on one question: will ordinary users pay for “AI team collaboration”?
If yes, ChainOpera has captured the next wave of AI application growth. If no, it becomes another case of “great tech, poor product.”
In fact, in the AI Agent space, we’ve already seen too many projects with “impressive demos, dismal business outcomes.” True winners are usually teams that package complex technology into simple experiences.
The final test is straightforward: after experiencing Agent Social’s team collaboration, would you ever want to go back to ChatGPT’s solo conversations?
Just like people accustomed to group chats can hardly accept returning to SMS-only communication.
ChainOpera’s Agent Social carries the mission of transforming multi-agent collaboration from academic concept into commercial reality. Whether it succeeds—we’ll know soon enough.
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