From trading gateway to execution hub, understanding SIA’s ‘web3 AI operating system’

Topping Binance DappBay charts for days and promoted by Aster before the holiday, how does SIA transform “smart money” into public infrastructure using composable agents?

Why is AI agent discussion resurging in early 2026?

An unavoidable industry variable is the commercialization progress of general-purpose AI agents validated by major tech giants. Meta’s multi-billion-dollar acquisition of Manus by late 2025 could mark a watershed moment, signaling that AI’s core value is poised to shift from “content generation” to “task execution and completion” in 2026.

But shifting focus back to web3, the challenges become more concrete, and even brutal: If AI fails to directly lower the barrier for on-chain operations,reduce user hopping between DApps, or make transactions more controllable, then no matter how hot the hype cycle gets, AI will struggle to break free from its dull “hype cycle.” 

Interestingly, right around the turn of the year, on-chain data captured a distinctly different trajectory from typical AI projects: 

• Binance DappBay’s new DApp daily active user rankings were dominated for consecutive days by the new project “SIA” (SIANEXX), which opened up a significant gap in scale compared to the second-place contender;
• Concurrently, Aster’s official Twitter account heavily promoted SIA’s “Smart Copy Trading” feature on Christmas Day, enabling users to execute one-click copy trades directly from SIA on Aster. Subsequently, on-chain trading volume for this module rapidly surged to millions of dollars.

In a cycle where AI × Web3 projects exhibit high homogeneity, why has SIA managed to fire the first shot of 2026? What is the underlying logic behind this explosive growth? 

I. Can ‘smart money’ be encapsulated as an API?

At the start of the new year, most blockchain players collectively witnessed Vida’s “super smart money” maneuver—amid BROCCOLI714’s abnormal volatility, Vida precisely captured an extremely brief window of opportunity, securing millions in profits.  

Such extraordinary cases are no longer rare. Arbitrage opportunities emerge almost daily on the blockchain. Yet for ordinary users, these opportunities are often blocked by two formidable barriers: 

• Information asymmetry leading to “invisibility”: By the time you catch wind of a hot topic through social media, professional addresses have already positioned themselves.
• Execution friction causing “inability to keep up”: Authorizations, slippage adjustments, transaction confirmations. Traditional UI interactions feel clunky and inefficient against the backdrop of rapidly shifting volatility.

Ultimately, the blockchain isn’t lacking in opportunities or high-probability addresses. What’s missing is the ability for ordinary users to consistently capture, replicate, and execute these opportunities. 

For most, failure doesn’t stem from misjudgment but from the execution process itself: paths are too long, steps too numerous, emotions fluctuate relentlessly, ultimately letting opportunities slip away amid cumbersome operations.

This is precisely why CZ publicly declared that “AI trading will be a massive frontier.” Objectively speaking, while the narrative of Crypto × AI has cycled through various iterations over the past two years (computing power, AI chains, agents, infrastructure, etc.), one reality remains unchanged: the operational complexity of web3 has not significantly decreased with the advent of AI.

From this perspective, AI agent explorations in the web2 world like Doubao Mobile and Manus offer valuable lessons. For web3, AI products that truly retain users in the future should not merely be “better at answering questions,” but rather highly integrated “service forms.” 

Especially at the on-chain operational level. Imagine this: what if AI didn’t just assist with analysis, but progressively broke down and encapsulated more trading decisions, handing them off to agents for continuous execution? Ultimately achieving round-the-clock market monitoring, signal detection, and active participation in trades. What would unfold?

This is precisely the question SIA, mentioned at the outset, seeks to address. As a cohesive team comprising seasoned Wall Street traders and top AI experts, its self-positioning is crystal clear: it aspires not merely to be a “smarter AI analysis tool,” but to become an AI Agent infrastructure and application platform for web3. Crucially, it pioneers AI deployment in the high-frequency, execution-intensive trading domain, a scenario with undeniable demand. 

Thus, “making trading effortless for all” is SIA’s core proposition. Its fundamental logic can be summarized in one sentence: Decompose trading expertise and strategies, previously held by a select few, into composable agents and empower ordinary users with them.

When AI truly begins “monitoring the market” and takes over portions of on-chain execution around the clock, the crypto market enters a new phase. 

II. When AI begins ‘on-chain monitoring’: SIA’s 24×7 on-chain trader network

To be fair, “AI trading” or “automated trading” isn’t a new concept in web3, and leveraging natural language processing (NLP) to replace cumbersome operations has long been a hot direction in the field. 

However, SIA’s core differentiation lies in its approach: rather than forcing users to adapt to complex specialized tools, it has built a composable intelligent network through AI Agents. In short, compared to projects still stuck in the whitepaper phase, SIA has achieved deep implementation in both trade execution and modular Agent creation. 

Its product matrix outlines a clear progression: from assisted information filtering (web3- exclusive GPT), to automated strategy generation (Agent Strategy Factory), to fully delegated execution (Smart Copy Trading).

1. Web3-exclusive chat agent: From basic Q&A to ‘deep investment assistance’ 

Fundamentally distinct from the ubiquitous “ChatGPT-clone” projects flooding the market, SIA’s Chat Agent functions more like a “crypto-version of Jarvis” with quantitative expertise, or, more accurately, an on-chain analytical frontend grounded in quantitative analysis.

It goes beyond merely reporting token prices. Its true competitive edge lies in integrating thousands of professional trading strategy models on the backend. This enables the Agent to instantly access on-chain data, technicalindicators, and capital flow paths, generating actionable analytical insights and delivering highly granular investment recommendations to users. 

For instance, when users inquire about a specific token, it delivers not vague trend assessments but a comprehensive breakdown based on real-time technical analysis (e.g., MA, RSI, MACD), on-chain smart money behavior, and liquidity structure. This includes the latest closing price, a summary of recent price movements, professional technical indicator analysis (MA/RSI/MACD), and price trend projections. 

This “professional strategy library + natural language interaction” model essentially condenses analytical capabilities traditionally reserved for elite traders into a toolkit accessible and understandable by ordinary users. This empowers average investors to become quasi-professional traders equipped with expert-level insights.

2. Barrier-free agent strategy factory: Democratizing trading capabilities

This is the most active and geek-centric feature in the SIA community. 

Under this architecture, trading strategies are no longer private assets but rather Agent units that can be created, fine-tuned, and reused. This signifies a shift from “privatization” to “democratization” in trading strategies. 

Within SIA’s Strategy Factory architecture, individual users need no programming or quantitative background. Simply input a natural language prompt to generate a custom Agent within one minute, leveraging over ten large language models. The current Marketplace already hosts hundreds of user-created Agents, ranging from functional tools and market monitoring/prediction modules to experimental and entertainment-oriented applications, including hardcore games like the AI-engine recreation of “The Legend of the Condor Heroes.” 

This diversity itself signals the early stages of a healthy agent ecosystem. 

SIA’s long-term vision is clear: to empower everyone with personalized agents that match their style and autonomously execute tasks. As the system evolves, these agents will gradually transform into users’ “on-chain trading digital avatars.” Even when users are offline, their agent will continuously scan for trading opportunities aligned with their logic 24/7.

3. Nanny-level execution: Smart copy trading × deep integration with the Aster ecosystem

Of course, what truly propels SIA’s rapid data growth in the short term is its execution layer design.

As an official deep partner of Aster, SIA has streamlined the complex on-chain order copying process into an extremely simplified workflow. Users need only deposit funds and click “Copy.” The AI Agent then continuously synchronizes signals and executes trades on the DEX.

This streamlined interaction has driven astonishing conversion rates. As mentioned earlier, following Aster’s pre-holiday promotion, trading volume surpassed millions of dollars within days. This not only retained Degen users but also captured significant attention from traditional financial institutions.

Notably, SIA has avoided the revenue-sharing mechanisms common in traditional copy trading. Instead, it prioritizes returning incentives to users and the ecosystem itself, the platform charges no revenue share, and users simultaneously qualify for dual airdrop rewards from both SIA and Aster.

Overall, SIA’s product logic does not directly generate trading strategies. Instead, it abstracts a large number of on-chain addresses with historically high win rates into pluggable, reusable execution units. When AI Agents begin actively “monitoring the market” and assume partial execution responsibilities on a 24/7 basis, a new form of participation emerges in the crypto market—the on-chain trader network.

III. Beyond trading tools: How to build web3’s AI operating system?

If dedicated Chat Agents, smart order copying, and Agent factories represent SIA’s vanguard for capturing market attention, the overall architecture outlined in its roadmap points toward a longer-term objective: building an AI operating system (AI OS) for Web3. 

In SIA’s vision, a mature and sustainable AI Agent ecosystem must address at least three foundational questions: Where does data come from? How are intents executed? How does value circulate within the system? 

Addressing these questions, SIA is progressively building a multi-layered system comprising a transaction layer, data layer, and Agent network. 

The first step is the web3 smart transaction layer, which is currently SIA’s most immediately implementable and user-perceivable tier. 

At this layer, SIA does not attempt to invent new trading markets. Instead, it uses AI Agents as a central hub to integrate operational paths previously scattered across different chains and trading venues. Users no longer need to understand “which chain, which protocol, or which path to take.” They simply express their transaction intent, and the system handles the decomposition and execution.

From a product perspective, this represents a repackaging of the transaction experience. Structurally, it also serves as the foundational layer underpinning all subsequent Agent collaboration and routing capabilities.

The second step is the web3 Super AI Agent, a concept specifically introduced above the transaction layer. 

This Agent transcends single-function limitations, aiming to encompass the entire core behavioral chain of web3 users: market analysis, strategy formulation, conversational order placement, portfolio management, smart money tracking, and even rapid scanning of meme-driven market trends.

More importantly, SIA does not treat trading capabilities as a closed module. Building upon the Super Agent foundation, users can further construct bespoke trading agents tailored to their risk preferences and investment styles. This enables the system to continuously execute predetermined logic 24/7, meaning trading no longer relies on user online status but gains characteristics of continuity and automation.

The third layer is the web3-exclusive AI data layer, as the ceiling of any AI ultimately depends on data quality.

Unlike general-purpose large models, SIA does not settle for public corpora. Instead, it builds a dedicated data foundation for web3: on one hand, consolidating industry-level knowledge structures through vector databases (RAG); on the other, dynamically absorbing on-chain fluctuations, protocol updates, and market sentiment shifts in real-time via the dynamic data layer (MCP).

The goal isn’t to make it better at chatting, but to gradually evolve the Agent into a vertical domain expert that truly understands web3’s operational logic, not just a generic question-answer model.

Finally, the Agent collaboration network represents SIA’s most visionary component. Under this concept, Agents will no longer operate in isolation but can be paid to collaborate and perform tasks collectively.

For example, theoretically, an Agent tasked with “monitoring public sentiment” could automatically request another Agent specializing in “executing transactions” to place an order upon detecting a signal. Each invocation and collaboration would be recorded, priced, and settled, fostering productivity synergy among Agents.

This mechanism elevates Agents beyond mere tools, endowing them with productive significance, Agents begin collaborating, and code starts directly generating value.

Of course, while SIA demonstrates exceptional PMF (Product-Market Fit), it must also confront the shared challenges of the AI + web3 space. This is not only SIA’s challenge but a question every project attempting to integrate AI into the crypto world must address:

• For instance, when tens of thousands of users simultaneously track the same batch of smart money addresses via SIA, could transaction congestion instantly erode profit margins?
• Or, after the TGE (Token Generation Event), how will SIA balance incentives with selling pressure? While its Rewards Hub currently demonstrates strong community stickiness, the future hinges on whether it can build a true deflationary loop through developer call fees and protocol revenue repurchases. 

Overall, “making trading effortless for all” represents SIA’s directional response.

Yet fragmented data, complex operational paths, and disjointed execution environments remain persistent challenges for web3. SIA’s approach isn’t to offer grand narrative solutions, but to deconstruct these structural problems into systematic engineering tasks that products can gradually tackle, a path requiring persistent refinement and incremental progress.

Final thoughts

Frankly speaking, AI trading cryptocurrency is not a new story. 

The truly novel variable lies in whether anyone will begin deconstructing “smart money” into pay-as-you-go, composable, and reusable on-chain primitives and transaction networks, enabling ordinary users to participate with minimal operational overhead. 

Looking back at the history of the internet, search engines changed the world not by creating information, but by significantly lowering the barriers to accessing and utilizing knowledge through “linking information.” Standing in the context of web3 in 2026, an equally critical question is emerging: Is it possible to systematically lower the barriers to interacting with and executing crypto assets by linking AI?

After all, only when users no longer need to repeatedly decipher addresses, authorizations, and protocol details, and users can simply instruct AI with ‘execute the strategy in my style’, can the explosive growth of scaled web3 × AI transactions truly materialize. 

Could agents become the new ‘Lego of liquidity’? Is SIA positioned at this pivotal moment? 2026—we’ll see.