Noya.ai Research Report: Outlook on Prediction Market Agents
Author: 0xjacobzhao | https://linktr.ee/0xjacobzhao
In previous Crypto AI series research reports, we have consistently emphasized the viewpoint that the most practically applicable scenarios in the current crypto field mainly focus on stablecoin payments and DeFi, with Agents being the key interface for the AI industry to users. Therefore, in the trend of the fusion of Crypto and AI, the two most valuable paths are: AgentFi based on existing mature DeFi protocols (basic strategies such as lending, liquidity mining, and advanced strategies like swap, Pendle PT, capital fee arbitrage) in the short term, and Agent Payment surrounding stablecoin settlement, relying on protocols such as ACP/AP2/x402/ERC-8004 in the medium to long term.
By 2025, prediction markets have become an industry trend that cannot be ignored, with total annual trading volume surging from about $9 billion in 2024 to over $40 billion in 2025, achieving more than 400% year-on-year growth. This significant growth is driven by multiple factors: macro-political events (such as the 2024 U.S. election) creating demand for uncertainty, the maturity of infrastructure and trading models, and the regulatory environment breaking new ground (Kalshi's legal victory and Polymarket's return to the U.S.). Prediction Market Agents are expected to emerge in early 2026, potentially becoming an emerging product form in the agent field in the coming year.
I. Prediction Markets: From Betting Tools to the 'Global Truth Layer'
Prediction markets are a financial mechanism for trading on the outcomes of future events, where contract prices essentially reflect the market's collective judgment on the probability of an event occurring. Their effectiveness arises from the combination of collective wisdom and economic incentives: in an environment where anonymous and real money is wagered, dispersed information is quickly integrated into price signals weighted by capital willingness, significantly reducing noise and false judgment.
By the end of 2025, the prediction market has basically formed a dual oligopoly structure led by Polymarket and Kalshi. According to Forbes, the total trading volume in 2025 is expected to reach approximately $44 billion, with Polymarket contributing about $21.5 billion and Kalshi approximately $17.1 billion. Kalshi has achieved rapid expansion thanks to its legal victory in the previous election contract case, its compliance first-mover advantage in the U.S. sports prediction market, and relatively clear regulatory expectations. Currently, the development paths of the two have shown clear differentiation:
Polymarket adopts a hybrid CLOB architecture with 'off-chain matching and on-chain settlement' and decentralized settlement mechanism, building a global, non-custodial high liquidity market; after compliance returns to the U.S., it forms a 'onshore + offshore' dual-track operation structure;
Kalshi integrates into the traditional financial system by accessing mainstream retail brokers through API, attracting Wall Street market makers for deep participation in macro and data contracts trading, while products are subject to traditional regulatory processes; long-tail demand and sudden events lag behind.
Apart from Polymarket and Kalshi, other competitive participants in the prediction market field mainly develop along two paths:
First, the compliant distribution path embeds event contracts into existing account systems of brokers or large platforms, relying on channel coverage, clearing capabilities, and institutional trust to establish advantages (for example, the collaboration between Interactive Brokers and ForecastEx's ForecastTrader, and FanDuel's collaboration with CME's FanDuel Predicts);
Second, the on-chain performance and capital efficiency path, taking Solana's perpetual contract DEX Drift as an example, which added a prediction market module B.E.T (prediction markets) to its existing product line.
The two paths of traditional financial compliance entry and crypto-native performance advantages together constitute a diverse competitive landscape for the prediction market ecology.
On the surface, prediction markets are similar to gambling, and essentially also a zero-sum game, but the core difference between the two does not lie in form but in whether they have positive externalities: by aggregating dispersed information through real-money trading, public pricing on real events creates a valuable signal layer. Despite limitations such as entertainment participation, its trend is shifting from gambling to a 'global truth layer'—with the integration of institutions such as CME and Bloomberg, event probabilities have become decision metadata that can be directly invoked by financial and enterprise systems, providing more timely and quantifiable market truths.
II. Prediction agents: architecture design, business model, and strategy analysis
Currently, prediction market agents (Prediction Market Agent) are entering the early practical stage, where their value lies not in 'AI predictions being more accurate' but in amplifying information processing and execution efficiency within prediction markets. Prediction markets are essentially information aggregation mechanisms, where prices reflect collective judgments on event probabilities; real-world market inefficiencies stem from information asymmetry, liquidity, and attention constraints. The reasonable positioning of prediction market agents is executable probabilistic portfolio management: transforming news, rule texts, and on-chain data into verifiable pricing deviations, executing strategies in a faster, more disciplined, and lower-cost manner, and capturing structural opportunities through cross-platform arbitrage and portfolio risk control.
An ideal prediction market agent can be abstracted into a four-layer architecture:
The information layer aggregates news, social, on-chain, and official data;
The analysis layer uses LLM and ML to identify mispricings and calculate Edge;
The strategy layer transforms Edge into positions using the Kelly formula, building positions in batches with risk control;
The execution layer completes multi-market orders, slippage and Gas optimization, and arbitrage execution, forming an efficient automated closed loop.
The ideal business model design for prediction market agents explores different directional spaces at different levels:
The underlying Infrastructure layer provides multi-source real-time data aggregation, Smart Money address database, unified prediction market execution engine, and backtesting tools, charging B2B/B2D for stable income unrelated to prediction accuracy;
The intermediate Strategy layer, either open-sourced or Token-Gated, sedimenting modular strategy components and community-contributed strategies, forming a combinable strategy ecosystem and achieving value capture;
The top-layer Agent layer runs real trades directly through entrusted management Vaults, with transparent on-chain records and 20-30% performance fees (plus a small management fee) realization capability.
The ideal prediction market agent is closer to an 'AI-driven probabilistic asset management product,' achieving returns through long-term disciplined execution and cross-market pricing games rather than relying on single prediction accuracy. The core logic of the design for a diversified income structure of 'infrastructure monetization + ecosystem expansion + performance participation' is that even if Alpha converges with market maturity, underlying capabilities such as execution, risk control, and settlement still hold long-term value and can reduce reliance on the single assumption of 'AI continuously defeating the market.'
Analysis of prediction market agent strategies:
Theoretically, Agents possess advantages in high speed, all-weather, and de-emotionalized execution, but in prediction markets, it is often difficult to convert to sustained Alpha, and their effective application is mainly limited to specific structures such as automated market making, cross-platform pricing capture, and long-tail event information integration; these opportunities are scarce and constrained by liquidity and capital.
Market selection: Not all prediction markets possess tradable value; participation value depends on five dimensions: settlement clarity, liquidity quality, information advantage, time structure, and manipulation risk. It is recommended to focus primarily on the early stages of new markets, long-tail events with few professional players, and temporary pricing windows caused by time zone differences; avoid highly heated political events, subjective settlement markets, and extremely low liquidity varieties.
Order strategy: Adopt a strict systematic position management. The entry prerequisite is that one's probability judgment is significantly higher than the market implied probability, and positions are determined according to a score-based Kelly formula (usually 1/10–1/4 Kelly), with the single-event risk exposure not exceeding 15%, to achieve long-term controlled risk, bearable drawdown, and compounding advantages.
Arbitrage strategy: Arbitrage in prediction markets mainly manifests in four categories: cross-platform price differences (need to be wary of settlement differences), Dutch Book arbitrage (high certainty but strict liquidity requirements), settlement arbitrage (dependent on execution speed), and correlated asset hedging (limited by structural mismatches). The key in practice is not to discover price differences but to strictly align contract definitions and settlement standards, avoiding pseudo-arbitrage caused by minor rule differences.
Smart money following: On-chain 'smart money' signals, due to lag, inducement risk, and sample issues, should not serve as the main strategy. A more reasonable usage is as a confidence adjustment factor, to assist in core judgments based on information and pricing deviations.
III. Noya.ai: The agent network from intelligence to action
As an early exploration of prediction market agents, NOYA's core concept is 'Intelligence That Acts.' In on-chain markets, mere analysis and insight are insufficient to create value—although dashboards, data analysis, and research tools can help users understand 'what might happen,' there still exists a large amount of manual operations, cross-chain friction, and execution risks between insight and execution. NOYA is built on this pain point: compressing the complete link of 'research → forming judgment → execution → continuous monitoring' in professional investment processes into a unified system, allowing intelligence to directly convert into on-chain actions.
NOYA achieves this goal by integrating three core levels:
Intelligence layer: Aggregating market data, token analysis, and prediction market signals.
Abstract layer: Hiding complex cross-chain routing, where users only need to express their intent (Intent).
Execution layer: AI Agent executes operations across chains and protocols based on user authorization.
In terms of product form, NOYA supports different participation modes from passive income users, active traders, and prediction market participants, and through designs like Omnichain Execution, AI Agents & Intents, and Vault Abstraction, modularizes and automates multi-chain liquidity management, complex strategy execution, and risk control.
The overall system forms a continuous closed loop: Intelligence → Intent → Execution → Monitoring, achieving efficient, verifiable, and low-friction transformation from insight to execution while ensuring users always maintain control of their assets.
IV. Noya.ai's product system and evolution path
Core cornerstone: Noya Omnichain Vaults
Omnivaults are NOYA's capital deployment layer, providing cross-chain and risk-controlled automated yield strategies. Users can simply deposit and withdraw assets, allowing the system to continuously operate across multiple chains and protocols without manual rebalancing or monitoring, with the core goal of achieving stable risk-adjusted returns rather than short-term speculation.
Omnivaults cover standard yield and loop strategies, clearly classified by asset and risk level, and support optional binding incentive mechanisms. At the execution level, the system automatically completes cross-chain routing and optimization and can introduce ZKML to provide verifiable proofs for strategy decisions, enhancing the transparency and credibility of automated asset management. The overall design is modular and combinable, supporting future access to more asset types and strategy forms.
The technical architecture of NOYA Vaults: each vault is uniformly registered and managed through the Registry, with the AccountingManager responsible for user shares (ERC-20) and net asset pricing; at the bottom, modular Connectors interface with protocols such as Aave and Uniswap to calculate cross-protocol TVL, relying on Value Oracle (Chainlink + Uniswap v3 TWAP) to complete price routing and valuation; trades and cross-chain operations are executed by Swap Handler (LiFi); ultimately, strategy execution is triggered by Keeper multi-signatures, forming a combinable, auditable execution closed loop.
Future Alpha: Prediction Market Agent
NOYA's most imaginative module: The intelligence layer continuously tracks on-chain capital behavior and changes in off-chain narratives, identifying news shocks, emotional fluctuations, and odds mismatches; when discovering probability deviations in prediction markets like Polymarket, the execution layer AI Agent can mobilize vault funds for arbitrage and rebalancing with user authorization. At the same time, Token Intelligence and Prediction Market Copilot provide users with structured token and prediction market analyses, directly transforming external information into executable trading decisions.
Prediction Market Intelligence Copilot
NOYA is committed to upgrading prediction markets from single-event betting to systematically manageable probabilistic assets. Its core module integrates diverse data such as market implied probabilities, liquidity structures, historical settlements, and on-chain smart money behavior, using expected value (EV) and scenario analysis to identify pricing deviations, while focusing on tracking high win-rate wallet position signals to distinguish information trading from market noise. Based on this, Copilot supports cross-market and cross-event correlation analysis and transmits real-time signals to AI Agents to drive automated execution of opening and adjusting positions, achieving portfolio management and dynamic optimization in prediction markets.
Core strategy mechanisms include:
Multi-source Edge information capture: Integrating Polymarket's real-time odds, polling data, private and external information streams to cross-verify event implied probabilities, systematically mining information advantages that have not been fully priced.
Cross-market and cross-event arbitrage (Prediction Market Arbitrage): Based on pricing differences between different markets, different contract structures, or similar events, constructing probabilistic and structural arbitrage strategies while controlling directional risk to capture odds convergence profits.
Odds-driven dynamic position management (Auto-adjust Positions): When odds significantly shift due to information, funds, or emotional changes, the AI Agent automatically adjusts position sizes and directions, achieving continuous optimization in prediction markets rather than one-time betting.
NOYA Intelligence Token Reports:
NOYA's institutional-level research and decision-making hub aims to automate the professional crypto investment research process and directly output decision-level signals usable for real asset allocation. This module presents clear investment positions, comprehensive ratings, core logic, key catalysts, and risk alerts in a standardized report structure, continuously updated in conjunction with real-time market and on-chain data. Unlike traditional research tools, NOYA's intelligence does not stop at static analysis but can be called, compared, and questioned through AI Agents using natural language, and directly delivered to the execution layer, driving subsequent cross-chain trading, capital allocation, and portfolio management, thus forming an integrated closed loop of 'research - decision - execution,' making Intelligence an active signal source in the automated capital operation system.
NOYA AI Agent (voice and natural language driven)
The NOYA AI Agent is the execution layer of the platform, with its core role being to directly convert user intentions and market intelligence into authorized on-chain actions. Users can express their goals through text or voice, and the Agent is responsible for planning and executing cross-chain, cross-protocol operations, compressing research and execution into a continuous process. It is a key product form for NOYA to lower the operational threshold for DeFi and prediction markets.
Users do not need to understand underlying links, protocols, or trading paths; they only need to express their goals through natural language or voice to trigger AI Agents to automatically plan and execute multi-step on-chain operations, realizing 'intent equals execution.' Under the premise of user signatures and non-custodial operation throughout, the Agent operates in a closed loop of 'intent understanding → action planning → user confirmation → on-chain execution → result monitoring,' not replacing decision-making but being responsible for efficient execution, significantly reducing friction and barriers to complex financial operations.
Trust moat: ZKML verifiable execution
Trusted execution aims to build a verifiable closed loop for the entire process of strategy, decision-making, and execution. NOYA introduces ZKML as a key mechanism to reduce trust assumptions: strategies are computed off-chain and generate verifiable proofs, triggering corresponding capital operations only after successful on-chain verification. This mechanism can provide credibility for strategy outputs without disclosing model details and supports verifiable backtesting and other derived capabilities. Currently, related modules are still marked as 'in development' in public documents, and engineering details are yet to be disclosed and validated.
Product roadmap for the next 6 months
Advanced order capabilities in prediction markets: Enhancing strategy expression and execution accuracy, supporting Agentized trading.
Extending to multiple prediction markets: Connecting to more platforms beyond Polymarket, expanding event coverage and liquidity.
Multi-source Edge information collection: Cross-verifying with market odds, systematically capturing unpriced probability deviations.
Clearer token signals and advanced reports: Outputting trading signals and in-depth on-chain analyses that can directly drive execution.
More advanced on-chain DeFi strategy combinations: Launching complex strategy structures, improving capital efficiency, returns, and scalability.
V. Noya.ai’s ecological growth and incentive system
Currently, Omnichain Vaults are in the early stages of ecological development, and their cross-chain execution and multi-strategy framework have been validated.
Strategy and coverage: The platform has integrated mainstream DeFi protocols such as Aave and Morpho, supporting cross-chain allocation of stablecoins, ETH, and their derivative assets, and has initially constructed layered risk strategies (e.g., basic yield vs. Loop strategies).
Development stage: Currently, the TVL scale is limited, with core objectives focused on functionality verification (MVP) and refining the risk control framework, with strong composability in architectural design, reserving interfaces for future introduction of complex assets and advanced Agent scheduling.
Incentive system: Kaito linkage and Space Race dual drive
NOYA has built a system anchored by 'real contribution' that deeply binds content narratives with liquidity growth flywheels.
Ecological cooperation (Kaito Yaps): NOYA enters Kaito Leaderboards with a composite narrative of 'AI × DeFi × Agent,' configuring a non-lockup incentive pool of 5% of the total supply, and additionally reserving 1% for the Kaito ecosystem. This mechanism deeply binds content creation (Yaps) with Vault deposits and Bond locking, transforming users' weekly contributions into Stars that determine levels and multipliers, thereby synchronously reinforcing narrative consensus and long-term capital stickiness at the incentive level.
Growth engine (Space Race): Space Race constitutes NOYA's core growth flywheel, replacing the traditional 'capital scale first' airdrop model with Stars as long-term equity certificates. This mechanism integrates Bond locking bonuses, reciprocal 10% referral incentives, and content dissemination into a weekly Points system, filtering out highly participative and consensus-driven long-term users, continuously optimizing community structure and token distribution.
Community building (Ambassador): NOYA adopts an invitation-based ambassador program, offering qualified participants community round participation qualifications and performance rebates based on actual contributions (up to 10%).
Currently, Noya.ai has accumulated over 3,000 on-chain users, with followers on the X platform exceeding 41,000, ranking in the top five on the Kaito Mindshare list. This indicates that NOYA has occupied a favorable attention ecological niche in the prediction market and Agent track.
Additionally, Noya.ai's core contracts have undergone dual audits via Code4rena and Hacken, and are integrated with Hacken Extractor.
VI. Token Economic Model Design and Governance
NOYA adopts a single-token ecological model, with $NOYA as the sole value carrier and governance vehicle.
NOYA adopts a buyback and burn value capture mechanism, realizing value generated in the protocol layer across AI Agents, Omnivaults, and prediction markets through mechanisms such as staking, governance, access rights, and buyback and burn, forming a usage → charging → buyback value closed loop, converting platform usage into long-term token value.
The project is based on the core principle of Fair Launch, without introducing angel rounds or VC investments, but rather through low valuations ($10M FDV) public community rounds (Launch-Raise), Space Race, and airdrops to complete distribution, deliberately reserving asymmetric upward space for the community, making the chip structure more biased towards active users and long-term participants; team incentives mainly come from long-term locked token shares.
Token Distribution
Total supply: 1 billion (1,000,000,000) NOYA
Initial circulating supply (Low Float): approximately 12%
Valuation and financing (The Raise): Financing amount: $1 million; valuation (FDV): $10 million
VII. Competitive Analysis of Prediction Agent Market
Currently, the prediction market agent (Prediction Market Agent) track is still in its early stages, with a limited number of projects; notable representatives include Olas (Pearl Prediction Agents), Warden (BetFlix), and Noya.ai.
From the perspective of product form and user participation, they represent three current pathways of the prediction market agent track:
1) Olas (Pearl Prediction Agents): Agent productization and runnable delivery, using 'run an automated prediction Agent' as the participation mode, encapsulating prediction market trading into runnable Agents: users invest and run, and the system automatically completes information acquisition, probability judgment, betting, and settlement. Additional installation participation modes have relatively limited user friendliness for ordinary users.
2) Warden (BetFlix): An interactive distribution and consumer-level betting platform that attracts users through low barriers and strong entertainment value in interactive experiences, adopting an interaction and distribution-oriented path, reducing participation costs through gamification and content front-end, emphasizing the consumption and entertainment properties of prediction markets. Its competitive advantage mainly comes from user growth and distribution efficiency, rather than strategy or execution depth.
3) NOYA.ai: Centered around 'capital custody + strategy execution,' abstracting prediction markets and DeFi execution into asset management products through Vaults, providing a low operation, low mental burden participation mode. If subsequent layers of Prediction Market Intelligence and Agent execution modules are added, it is expected to form an integrated workflow of 'research - execution - monitoring.'
Compared to projects like Giza and Almanak, which have achieved clear product delivery, NOYA's DeFi Agent is still in a relatively early stage. However, NOYA's differentiation lies in its positioning and entry-level: it enters the same execution and asset management narrative track with a fair launch valuation of approximately $10M FDV, currently possessing significant valuation discounts and growth potential.
NOYA: An asset management encapsulation AgentFi project centered around Omnichain Vaults, currently focusing on foundational infrastructure layers such as cross-chain execution and risk control, while the upper-layer Agent execution, prediction market capabilities, and ZKML-related mechanisms are still in development and verification stages.
Giza: Capable of directly running asset management strategies (ARMA, Pulse), currently with the highest completion level of AgentFi products.
Almanak: Positioned as AI Quant for DeFi, outputting strategy and risk signals through models and quantitative frameworks, primarily targeting professional capital and strategy management needs, emphasizing the systematic methodology and reproducibility of results.
Theoriq: A strategy and execution framework centered around multi-agent collaboration (Agent Swarms), emphasizing a scalable Agent collaboration system and a medium to long-term infrastructure narrative, leaning more towards foundational capability building.
Infinit: An execution-layer Agentic DeFi terminal, significantly lowering the execution threshold for complex DeFi operations through the process orchestration of 'intent → multi-step on-chain operations,' making users' perception of product value relatively direct.
VIII. Conclusion: Business logic, engineering implementation, and potential risks
Business logic:
NOYA is a relatively rare AI Agent × Prediction Market × ZKML multi-narrative overlay in the current market, further combining the product direction of Intent-driven execution. In terms of asset pricing, it starts with an approximate $10M FDV, significantly lower than the typical valuation range of $75M–$100M for similar AI / DeFAI / Prediction-related projects, forming a certain structural price difference.
From a design perspective, NOYA attempts to unify strategy execution (Vault / Agent) and information advantages (Prediction Market Intelligence) into the same execution framework and establish a value capture closed loop through protocol revenue return (fees → buyback & burn). Despite the project still being in its early stages, its risk-reward structure is more akin to a high-odds, asymmetric gaming target due to the combined effects of multi-narrative overlays and undervalued starting points.
Engineering implementation: At the verifiable delivery level, the core function currently launched by NOYA is Omnichain Vaults, providing cross-chain asset scheduling, yield strategy execution, and delayed settlement mechanisms, with relatively basic engineering implementation. The envisioned Prediction Market Intelligence (Copilot), NOYA AI Agent, and ZKML-driven verifiable execution are still in development, and have not yet formed a complete closed loop on the mainnet. It is not yet a mature DeFAI platform.
Potential risks and points of concern
Delivery uncertainty: The technical span from 'basic Vault' to 'all-purpose Agent' is vast, and caution should be exercised regarding the risk of roadmap delays or ZKML implementations falling short of expectations.
Potential systemic risks:Includes contract security, cross-chain bridge failures, and features unique to prediction markets.Oracle disputes(e.g., rules ambiguity leading to inability to adjudicate), any single point of failure may cause financial losses.
Disclaimer: This article has utilized AI tools such as ChatGPT-5.2, Gemini 3, and Claude Opus 4.5 during the creation process. The author has made efforts to proofread and ensure the information is true and accurate, but there may still be oversights, for which we request your understanding. It is particularly noted that the crypto asset market generally experiences divergence between project fundamentals and secondary market price performance. The content of this article is intended for information integration and academic/research exchange only, does not constitute any investment advice, nor should it be regarded as a recommendation for the buying or selling of any tokens.