DeAgent AI has chosen a path to enter the prediction market through AI oracles and agent infrastructure.
Written by: ChandlerZ, Foresight News
If human society has always had a curiosity and bets on the future, then crypto-native prediction markets are transforming this ancient demand into a public good that is quantifiable, liquid, and reusable. Over the past decade, the democratization of information has been achieved through the internet; in the Web3 and crypto space, values and beliefs are also being tokenized and priced, forming a more verifiable and incentive-compatible value democratization. The addition of AI has expanded the boundaries of prediction from simple price feeding to more complex judgments and rulings, giving predictions a sense of infrastructure, eliminating speculative interpretations. Prediction markets serve as the foundational information cornerstone for governance, hedging, and resource allocation. Google's integration of Polymarket and Kalshi's market probabilities into Google Finance starting in November 2025 marks the entry of prediction data into the public layer with hundreds of millions of users, which is both an endorsement of the industry and a signal of incremental demand.
Why Prediction Markets are a Battleground in Web3
The essence of prediction markets is to aggregate the tacit knowledge dispersed in individual minds into public probabilities through pricing. This idea can be traced back to Robin Hanson's Futarchy (governance by betting), where the value goals are determined by voting, and factual judgments are priced by the market, with prediction markets set as the primary mechanism for information aggregation. Research in academia has also shown that prediction markets outperform simple polls in many scenarios, especially in dynamic updates and incentive constraints.
When shifting the perspective from theoretical reasoning to real markets, you will find that this mechanism of aggregating cognition through pricing is being voted on by funds and users in 2024-2025. Prediction platforms represented by Polymarket and Kalshi have seen daily trading volumes approaching or even exceeding $100 million, with cumulative transaction volumes soaring to hundreds of billions, marking the transition of prediction markets from niche experiments to full-scale explosions. Data shows that Polymarket's monthly active traders reached a historical high of 477,850 in October, surpassing the previous record of 462,600 set in January. Its monthly trading volume rebounded to a record $3.02 billion last month, after hovering around or below $1 billion from February to August. The number of new markets opened on the platform in October reached 38,270, nearly three times that of August. Polymarket's trading volume, active trader count, and new market openings all hit historical highs in October. Kalshi's trading volume even surpassed Polymarket in October, reaching $4.4 billion.
In addition, with the shift in U.S. regulation and the acquisition of regulated entities, the path for compliance to return to the U.S. is becoming clearer. This series of events collectively indicates that the information derivatives market centered on predictions has a real, strong demand recognized by mainstream entry points.
From the perspective of application spillover, prediction markets can be seen as a universal risk hedging and governance module. Enterprises can hedge operational risks against the probability of policy implementation, DAOs can bind proposals and KPIs with conditional markets, and media and platforms can use probability narratives as a new layer of information display. The integration of information entry points like Google and Perplexity with prediction platforms is accelerating this era where probability equals interface.
The Investor Dilemma Amidst Track Prosperity: Usable but Not Investable
When a track enters early-stage explosion, ordinary investors usually ask two questions. One is whether the demand is real, and the other is how to share in the growth. We have already seen the answer to the former; however, the latter has long been trapped in an awkward reality in the prediction track: leading products are usable but not investable.
Taking Polymarket as an example, its official statement once indicated that the project had no tokens and had not announced any airdrop or TGE plans. Although recently, Polymarket's Chief Marketing Officer Matthew Modabber confirmed the POLY token and airdrop plans. Earlier in October, the company's founder Shayne Coplan also revealed that they would launch the POLY token. But this still means that for investors who did not deeply participate in Polymarket early on, the most lucrative and asymmetrical original bonus period has essentially been consumed in advance. Now, unless you personally participate in every event market, it is difficult to gain track-level beta exposure and align long-term returns. For investors hoping to hold track growth in an index-like manner, the targets are extremely scarce.
More broadly, regulated event contract platforms like Kalshi also do not have crypto-native tokens; other on-chain prediction applications or tools either lack the scale and network effects to serve as industry indices or resemble single-function tools that cannot carry track-level value attribution. The result is that demand is blooming fiercely at the application layer, while the investment layer faces a structural gap with no tokens to invest in.
From Pump.fun and Virtuals, Looking at Polymarket and DeAgent AI
Reflecting on the Meme track of 2024, one of the most representative phenomena is the breakout of Pump.fun, with its extremely low entry barrier and standardized curve issuance mechanism igniting the zero-to-one creation on-chain. In its early explosive phase, the platform itself had no native token, and users could only share in the prosperity through individual stock-like bets on each meme. Subsequently, the market saw the emergence of a token vehicle, Virtuals (VIRTUAL), that could index this ecological heat. VIRTUAL binds key paths such as creation, trading, and LP pairing within the ecosystem to the platform token, making holding VIRTUAL akin to holding a growth index of the entire Agent/Meme ecosystem, thus capturing the premium released by Pump.fun in both narrative and fundamentals.
Pump.fun launched its platform token PUMP in the latter half of 2025, but the timing was later, and its value capture logic misaligned with the earlier ecological explosion. Historical experience tells us that when the application layer explodes first without an index asset, the foundational infrastructure projects that provide both existing products and tokens often outperform the average of the track in value reassessment.
Returning to the emerging prediction market track, DeAgent AI plays such an infrastructural role. DeAgent AI is an AI agent infrastructure covering the Sui, BSC, and BTC ecosystems, empowering AI agents to achieve trustless autonomous decision-making on-chain. It aims to address three major challenges faced by AI in distributed environments: identity authentication, continuity assurance, and consensus mechanisms, building a trustworthy AI agent ecosystem.
DeAgent AI has constructed a set of underlying protocols centered around prediction markets and DeFi scenarios, with AI oracles and multi-agent execution networks at its core. One end connects real-world and on-chain data, standardizing complex judgments, rulings, and signal production into verifiable oracle outputs, while the other end integrates these outputs into trading, governance, and derivative design through the agent network, thus becoming the information and value hub of the entire track.
Because of this, this framework is currently being mirrored in the prediction market track. Polymarket corresponds to the Pump.fun of that year (a product leader but long lacking investable tokens), while DeAgent AI (AIA) plays the role of a value container similar to Virtuals. It not only provides the key infrastructure modules missing in prediction markets (AI oracles and agent execution networks) but also offers a publicly tradable token AIA as an anchor for track indexation, allowing investors to indirectly share in the medium- to long-term growth of the entire prediction track by holding AIA.
How DeAgent AI Becomes the Value Container of the Prediction Track
The technical framework of DeAgent AI focuses on solving the three fundamental challenges of continuity, identity, and consensus faced by decentralized AI agents operating on-chain. Through a state system combining hot memory and long-term memory, as well as on-chain state snapshots, agents will not be reset in multi-chain and multi-task scenarios, and their behaviors and decisions will have a complete, traceable lifecycle; using a unique on-chain identity + DID and hierarchical authorization mechanisms ensures that each agent's identity is non-falsifiable; and employing minimum entropy decision-making and validator consensus converges the chaotic outputs of multiple models into verifiable, deterministic results. On this basis, the A2A protocol is responsible for standardized collaboration between agents, and the MPC execution layer ensures the privacy and security of sensitive operations, ultimately integrating identity, security, decision-making, and collaboration into a verifiable, scalable decentralized AI agent infrastructure.
AlphaX and CorrAI's Dual Landing
At the application layer, AlphaX and CorrAI are the most intuitive realizations of this infrastructure. AlphaX is the first AI model developed based on the feedback training mechanism of DeAgent AI, incubated by its community, utilizing Transformer architecture, Mixture-of-Experts (MoE) technology, and human feedback reinforcement learning (RHF) mechanisms, focusing on improving the accuracy of cryptocurrency price predictions. AlphaX predicts crypto price trends for 2-72 hours with an accuracy rate of 72.3%, achieving +18.21% and +16.00% ROI in real trading simulations in December 2024 and January 2025, respectively, with a win rate of around 90%, demonstrating the considerable practicality of AI predictions in real trading environments.
CorrAI, on the other hand, is more like a no-code Copilot for DeFi/quantitative users, helping users select strategy templates, adjust parameters, conduct backtesting, and issue on-chain instructions, creating a closed loop between seeing signals and executing strategies, while also bringing more real funds and actions into the DeAgent AI agent network.
On the ecological side, AlphaX has already accumulated a considerable number of users and interactions through activities and integrations on public chains like Sui and BNB. Coupled with multiple chains and various application scenarios, the overall network of DeAgent AI has formed billions of on-chain interactions and tens of millions of user relationships, no longer remaining an experimental project confined to white papers, but a real, operational, and continuously invoked infrastructure.
From Price Feeding to Subjective Judgment AI Oracles
Traditional oracles mainly handle objective values like BTC/USD, achieving consensus through multi-node redundancy and data source aggregation; once the question shifts to subjective/non-deterministic judgments (e.g., "Is ETH more likely to rise or fall this weekend?"), nodes each call large models, and the answers often do not align, making it difficult to prove that a specific model was indeed called and yielded that result, leading to a breakdown in safety and trust.
DeAgent AI has designed the DeAgentAI Oracle specifically for such subjective questions from the outset. Users submit questions in the form of multiple-choice and pay a service fee. Multiple AI Agents in the network independently judge based on retrieval and reasoning, then vote, with the on-chain contract aggregating the votes to select the final result and record it on-chain. This way, the originally divergent AI outputs are compressed into verifiable deterministic results, replacing the belief in a certain node with the verification of a publicly available voting and recording process, making AI judgment a public service that can be repeatedly called on-chain for the first time, which is very suitable for scenarios like prediction markets, governance decisions, and InfoFi. This component is currently undergoing internal testing.
In specific cases, DeAgent AI's Agents have been used to provide judgments around real-world events. Recently, during the U.S. federal government shutdown, the team constructed a decision tree model at the end of October based on market pricing from platforms like Kalshi and Polymarket, combined with historical shutdown durations, the structure of bipartisan negotiations, and key time nodes. The conclusion reached was that the current shutdown is most likely to end between November 12-15 (or in the nearby range of November 13-20), rather than the commonly seen narrative of an endless tug-of-war in market sentiment.
At the same time, regarding the controversial topic of "whether Bitcoin has entered a bear market," DeAgent AI integrated on-chain data, ETF fund flows, macro policy shifts, and technical indicator divergences to determine that the current phase is closer to "a deep adjustment in the early stages of a bear market," rather than an accelerating bull market that has not yet ended, and provided key price levels and risk monitoring frameworks based on this.
This type of prediction and judgment around specific topics not only demonstrates DeAgent AI's oracle's ability to deconstruct and integrate subjective and complex issues but also indicates that its outputs can now be directly transformed into usable signals for prediction markets and trading decisions, rather than merely remaining at the demonstration level.
How AIA Indexes Track Growth
From the investor's perspective, the value capture logic of AIA lies in the fact that it serves as both the payment and settlement medium for DeAgentAI Oracle and Agent networks, as well as the staking asset and governance certificate for nodes and validators. As more prediction applications, governance modules, and DeFi strategies connect to this network, the number of requests, call frequency, and security needs will translate into actual demand for AIA, naturally binding its value to the overall usage of the track, rather than merely relying on one-time narrative hype.
More critically, this value chain is itself closed-loop and deducible. As prediction applications like Polymarket expand market categories and introduce more complex subjective issues, they will need to rely on AI oracles for complex judgments; these calls will directly reflect the growing demand for AI oracle infrastructure like DeAgent AI. When the usage of the Oracle/Agent network increases, the associated functional token AIA, as a payment, settlement, and staking asset, will also see its demand and value rise accordingly. In other words, if you believe that prediction markets will continue to expand, it is hard not to simultaneously believe that the demand for AI oracles will grow, which will ultimately be reflected in AIA's long-term pricing.
From an asset attribute perspective, AIA meets both the "functional" and "investable" conditions. On one hand, it corresponds to an AI oracle and agent infrastructure aimed at subjective issues, directly addressing the core pain points of prediction markets; on the other hand, it is itself a token asset that can be allocated in the public market. In comparison, platforms like Kalshi and Polymarket still do not have native tokens available for investment, while traditional price oracles do have tokens but serve the objective price feeding track, which is not the same value chain as AI-driven subjective oracles. In the niche of AI oracles + tradable tokens, AIA is currently one of the few, if not the only, assets that can satisfy both usability and investability, thus having the opportunity to become the most direct indexation vehicle for growth in the prediction track.
How to Participate in the Prediction Track?
The current prediction track has clearly entered a stage where application stories are told in the foreground, and value gradually sinks to the background. Polymarket and Kalshi have proven the existence of the track with real trading volumes, while what can truly be priced long-term is likely the layer that supports the operation of these applications, namely the AI oracles and agent networks responsible for judgment and settlement, along with the functional tokens tied to them.
As prediction applications attempt to carry more complex and subjective judgments, they will inevitably generate higher and more frequent demand for AI oracles; this demand will ultimately settle into the continuous use of infrastructure like DeAgent AI. The functional tokens closely tied to the payment, settlement, and staking of this infrastructure will also carry corresponding value in this process. Therefore, the real question to consider next is not whether to participate in this track, but rather how and at what level to participate in it.
A relatively clear approach is to engage at the application layer with participation and at the infrastructure layer with positions. At the application layer, users can continue to use platforms like Polymarket as tools to gain Alpha, betting on specific events with their positions; at the infrastructure layer, they can align with the long-term proposition of AI oracles becoming standard in prediction markets by moderately allocating AIA. The former answers whether one can make money in this round, while the latter addresses whether one will be lifted alongside the underlying when this track grows.
Of course, AIA is just one factor in the combination and not a substitute for risk control. A more prudent approach is to view it as part of the prediction track infrastructure index, giving this long-term logic a position and time within one's risk budget, allowing the market to validate the judgment of this narrative.
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