Noya.ai Research Report: A Forecast on Market Intelligence Agents

Written by: 0xjacobzhao

In our previous Crypto AI series reports, we have consistently emphasized the viewpoint that the most practically valuable scenarios in the current crypto space are primarily concentrated on stablecoin payments and DeFi, while Agent is the key interface for the AI industry facing users. Therefore, in the trend of the integration of Crypto and AI, the two most valuable paths are: AgentFi, based on existing mature DeFi protocols (basic strategies such as lending, liquidity mining, etc., and advanced strategies like Swap, Pendle PT, and funding rate arbitrage) in the short term, and Agent Payment, focusing on stablecoin settlement and relying on protocols like ACP/AP2/x402/ERC-8004 in the medium to long term.

Prediction markets have become an industry trend that cannot be ignored by 2025, with annual total trading volume surging from about $9 billion in 2024 to over $40 billion in 2025, achieving a year-on-year growth of over 400%. This significant growth is driven by multiple factors: uncertainty demand brought by macro-political events (such as the 2024 U.S. elections), the maturity of infrastructure and trading models, and the thawing of the regulatory environment (Kalshi's legal victory and Polymarket's return to the U.S.). Prediction Market Agents are expected to present early prototypes in early 2026 and may become an emerging product form in the agent field in the coming year.

I. Prediction Markets: From Betting Tools to "Global Truth Layer"

Prediction markets are a financial mechanism for trading around the outcomes of future events, where contract prices essentially reflect the market's collective judgment on the probability of an event occurring. Their effectiveness stems from the combination of collective wisdom and economic incentives: in an environment where anonymous, real-money bets are placed, dispersed information is quickly integrated into price signals weighted by capital willingness, significantly reducing noise and false judgments.

By the end of 2025, prediction markets have basically formed a duopoly dominated by Polymarket and Kalshi. According to Forbes, the total trading volume in 2025 is expected to reach about $44 billion, with Polymarket contributing approximately $21.5 billion and Kalshi around $17.1 billion. Kalshi has achieved rapid expansion due to its previous legal victory in election contracts, its compliance-first 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 a decentralized settlement mechanism, building a global, non-custodial, high-liquidity market, forming a "onshore + offshore" dual-track operational structure after compliance returns to the U.S.;
• Kalshi integrates into the traditional financial system by connecting with mainstream retail brokers through APIs, attracting Wall Street market makers to deeply participate in macro and data-driven contract trading, with products constrained by traditional regulatory processes, leading to relatively delayed long-tail demand and sudden events.

In addition to Polymarket and Kalshi, other competitive players in the prediction market space are developing along two paths:

• One is the compliance distribution path, embedding event contracts into the existing account systems of brokers or large platforms, relying on channel coverage, clearing capabilities, and institutional trust to establish advantages (e.g., ForecastTrader in collaboration with Interactive Brokers and FanDuel Predicts in collaboration with CME);
• The other is the on-chain performance and capital efficiency path, exemplified by the perpetual contract DEX Drift in the Solana ecosystem, which has added a prediction market module B.E.T (prediction markets) based on its existing product line.

These two paths of traditional financial compliance entry and crypto-native performance advantages together form a diverse competitive landscape for the prediction market ecosystem.

On the surface, prediction markets resemble gambling and are essentially a zero-sum game, but the core difference between the two lies not in form but in whether they have positive externalities: by aggregating dispersed information through real-money transactions, they publicly price real events, forming a valuable signal layer. Despite limitations such as entertainment participation, the trend is shifting from gaming to a "global truth layer"—as institutions like CME and Bloomberg come on board, event probabilities have become decision metadata that can be directly invoked by financial and corporate systems, providing more timely and quantifiable market truths.

II. Prediction Agents: Architecture Design, Business Models, and Strategy Analysis

Currently, Prediction Market Agents are entering the early practice 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 to execute strategies faster, more disciplined, and at lower costs, while capturing structural opportunities through cross-platform arbitrage and portfolio risk control.

An ideal prediction market agent can be abstracted into a four-layer architecture:

• Information Layer gathers news, social, on-chain, and official data;
• Analysis Layer uses LLM and ML to identify mispricing and calculate Edge;
• Strategy Layer converts Edge into positions through Kelly's formula, batch positioning, and risk control;
• Execution Layer completes multi-market ordering, slippage and gas optimization, and arbitrage execution, forming an efficient automated closed loop.

The ideal business model design for prediction market agents has different exploratory spaces at different levels:

• Bottom Layer Infrastructure: providing multi-source real-time data aggregation, Smart Money address databases, a unified prediction market execution engine, and backtesting tools, charging B2B/B2D for stable income unrelated to prediction accuracy;
• Middle Layer Strategy: sedimenting modular strategy components and community-contributed strategies in an open-source or token-gated manner, forming a combinable strategy ecosystem and achieving value capture;
• Top Layer Agent: directly running real trades through a managed Vault, with transparent on-chain records and a performance fee of 20-30% (plus a small management fee) for 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 mispricing games, rather than relying on single prediction accuracy. The core logic of the diversified income structure design of "infrastructure monetization + ecosystem expansion + performance participation" lies in the fact that even if Alpha converges as the market matures, underlying capabilities such as execution, risk control, and settlement still hold long-term value, reducing dependence on the single assumption of "AI continuously beating the market."

~Prediction Market Agent Strategy Analysis:~

In theory, agents possess advantages in high-speed, round-the-clock, and de-emotionalized execution, but in prediction markets, they often struggle to translate into sustained Alpha, with effective applications mainly limited to specific structures such as automated market making, cross-platform mispricing capture, and information integration for long-tail events, which 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 prioritize early-stage new markets, long-tail events with few professional players, and temporary pricing windows caused by time zone differences; avoid high-heat political events, subjective settlement markets, and extremely low liquidity varieties.
• Ordering Strategy: Adopt strict systematic position management. The entry premise is that one's probability judgment is significantly higher than the market's implied probability, and position size is determined based on the scored Kelly formula (usually 1/10--1/4 Kelly), with single event risk exposure not exceeding 15%, to achieve controlled risk, bearable drawdown, and compounding advantages for steady growth in the long term.
• Arbitrage Strategy: Arbitrage in prediction markets mainly manifests in four types: cross-platform price differences (be wary of settlement differences), Dutch Book arbitrage (high certainty but strict liquidity requirements), settlement arbitrage (relying on execution speed), and hedging correlated assets (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 subtle rule differences.
• Smart Money Following: On-chain "smart money" signals are not suitable as a main strategy due to lag, inducement risks, and sample issues. A more reasonable use is as a confidence adjustment factor to assist in core judgments based on information and pricing deviations.

III. Noya.ai: An 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 insights are not enough to create value—while dashboards, data analysis, and research tools can help users understand "what might happen," there remains a significant amount of manual operation, cross-chain friction, and execution risk between insight and execution. NOYA is built on this pain point: compressing the complete link of "research → forming judgments → execution → continuous monitoring" in professional investment processes into a unified system, allowing intelligence to be directly transformed into on-chain actions.

NOYA achieves this goal by integrating three core layers:

• Intelligence Layer: Aggregating market data, token analysis, and prediction market signals.
• Abstraction Layer: Hiding complex cross-chain routing, allowing users to simply express intent.
• Execution Layer: AI Agents execute operations across chains and protocols based on user authorization.

In terms of product form, NOYA supports different participation methods for 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 over their assets.

IV. Noya.ai's Product System and Evolution Path

Core Foundation: Noya Omnichain Vaults

Omnivaults are NOYA's capital deployment layer, providing cross-chain, risk-controlled automated yield strategies. Users can continuously run their assets across multiple chains and protocols through simple deposit and withdrawal operations, without the need for manual rebalancing or monitoring, with the core goal of achieving stable risk-adjusted returns rather than short-term speculation.

Omnivaults cover strategies such as standard yield and loop, clearly divided by asset and risk levels, and support optional binding incentive mechanisms. On the execution side, the system automatically completes cross-chain routing and optimization, and can introduce ZKML to provide verifiable proof for strategy decisions, enhancing the transparency and credibility of automated asset management. The overall design focuses on modularity and combinability, supporting future access to more asset types and strategy forms.

The technical architecture of NOYA Vaults: Each vault is uniformly registered and managed through a Registry, with AccountingManager responsible for user shares (ERC-20) and net asset pricing; the underlying layer connects to protocols like Aave and Uniswap through modular Connectors and calculates cross-protocol TVL, relying on Value Oracle (Chainlink + Uniswap v3 TWAP) to complete price routing and valuation; transactions and cross-chain operations are executed by Swap Handler (LiFi); finally, strategy execution is triggered by Keeper multi-signature, forming a combinable and auditable execution closed loop.

~Future Alpha: Prediction Market Agents~

The most imaginative module of NOYA: the intelligence layer continuously tracks on-chain capital behavior and changes in off-chain narratives, identifying news shocks, emotional fluctuations, and odds mismatches; when probability deviations are discovered in prediction markets like Polymarket, the execution layer AI Agent can mobilize vault funds for arbitrage and rebalancing under user authorization. Meanwhile, 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 aims to upgrade 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 behaviors, using expected value (EV) and scenario analysis to identify pricing deviations, while focusing on tracking position signals from high-win-rate wallets 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, driving automated execution of opening and rebalancing positions, achieving portfolio management and dynamic optimization in prediction markets.

Core strategy mechanisms include:

• Multi-source Edge Information Capture: Integrating real-time odds from Polymarket, 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: Constructing probabilistic and structural arbitrage strategies based on pricing differences between different markets, different contract structures, or similar events, capturing odds convergence returns while controlling directional risks.
• Odds-driven Dynamic Position Management: When odds significantly deviate due to information, capital, or emotional changes, the AI Agent automatically adjusts position size and direction, achieving continuous optimization in prediction markets rather than one-off bets.

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 warnings in a standardized report structure, continuously updated with real-time market and on-chain data. Unlike traditional research tools, NOYA's intelligence does not stop at static analysis but can be invoked, compared, and questioned through AI Agents in natural language, and directly delivered to the execution layer, driving subsequent cross-chain transactions, capital allocations, 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, primarily responsible for directly transforming user intent and market intelligence into authorized on-chain actions. Users can express 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 the underlying chains, protocols, or trading paths; they only need to express their goals through natural language or voice to trigger the AI Agent to automatically plan and execute multi-step on-chain operations, achieving "intent equals execution." Under the premise of full user signature and non-custodial, the Agent operates in a closed loop of "intent understanding → action planning → user confirmation → on-chain execution → result monitoring," not replacing decision-making but efficiently executing, significantly reducing the friction and barriers of complex financial operations.

Trust Moat: ZKML Verifiable Execution

Verifiable execution aims to build a fully verifiable closed loop for 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, which can only trigger corresponding fund operations after on-chain verification. This mechanism can provide credibility for strategy outputs without disclosing model details and supports derivative capabilities such as verifiable backtesting. Currently, related modules are still marked as "in development" in public documents, and engineering details are yet to be disclosed and verified.

Future 6-Month Product Roadmap

• Advanced Order Capabilities for Prediction Markets: Enhancing strategy expression and execution accuracy, supporting agent-based trading.
• Expansion to Multiple Prediction Markets: Integrating more platforms beyond Polymarket to expand event coverage and liquidity.
• Multi-source Edge Information Collection: Cross-verifying with market odds to systematically capture underpriced 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 to enhance capital efficiency, returns, and scalability.

V. Noya.ai's Ecosystem Growth and Incentive System

Currently, Omnichain Vaults are in the early stages of ecosystem development, and their cross-chain execution and multi-strategy framework have been validated.

• Strategies and Coverage: The platform has integrated mainstream DeFi protocols like Aave and Morpho, supporting cross-chain allocation of stablecoins, ETH, and their derivatives, and has initially constructed layered risk strategies (e.g., basic yield vs. loop strategies).
• Development Stage: The current TVL is limited, with the core goal being functional validation (MVP) and refining the risk control framework, with a strong combinability in architectural design, reserving interfaces for the subsequent introduction of complex assets and advanced agent scheduling.

~Incentive System: Kaito Interaction and Space Race Dual Drive~

NOYA has built a set of mechanisms anchored in "real contributions," deeply binding content narratives and liquidity growth flywheels.

• Ecosystem Cooperation (Kaito Yaps): NOYA lands on Kaito Leaderboards with a composite narrative of "AI × DeFi × Agent," allocating a 5% non-locked incentive pool of total supply and reserving an additional 1% for the Kaito ecosystem. Its mechanism deeply binds content creation (Yaps) with Vault deposits and Bond locking, converting users' weekly contributions into Stars that determine levels and multipliers, thus synchronously strengthening narrative consensus and long-term capital stickiness at the incentive level.
• Growth Engine (Space Race): Space Race constitutes NOYA's core growth flywheel, using Stars as long-term equity certificates to replace traditional "capital scale priority" airdrop models. This mechanism integrates Bond locking bonuses, a two-way 10% referral incentive, and content dissemination into a weekly Points system, selecting highly engaged 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, and its X platform followers have surpassed 41,000, ranking in the top five on the Kaito Mindshare leaderboard. This indicates that NOYA has secured a favorable attention ecological position in the prediction market and agent track.

Additionally, Noya.ai's core contracts have undergone dual audits by Code4rena and Hacken and have integrated 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 employs a buyback and burn value capture mechanism, where the value generated in products like AI Agents, Omnivaults, and prediction markets is realized through mechanisms such as staking, governance, access rights, and buyback and burn, forming a closed loop of usage → charging → buyback value, 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 instead distributing through a publicly raised community round (Launch-Raise) at a low valuation ($10M FDV), Space Race, and airdrops, deliberately reserving asymmetric upside space for the community, making the chip structure more inclined 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 Circulation (Low Float): About 10%

Valuation and Financing (The Raise): Amount Raised: $1 million; Valuation (FDV): $10 million

VII. Prediction Agent Market Competition Analysis

Currently, the Prediction Market Agent track is still in its early stages, with a limited number of projects, among which the more representative include Olas (Pearl Prediction Agents), Warden (BetFlix), and Noya.ai.

From the perspective of product forms and user participation methods, they represent three paths currently in the prediction market agent track:

• 1) Olas (Pearl Prediction Agents): Agent productization and operational delivery, with participation in "running an automated prediction agent," encapsulating prediction market trading into a runnable agent: users fund and run, and the system automatically completes information acquisition, probability judgment, betting, and settlement. The need for additional installations makes it relatively less user-friendly for ordinary users.
• 2) Warden (BetFlix): An interactive distribution and consumer-grade platform, attracting user participation through low barriers and strong entertainment experiences, adopting an interactive and distribution-oriented path to lower participation costs through gamification and content-driven front ends. Its competitive advantage mainly comes from user growth and distribution efficiency, rather than depth in strategy or execution.
• 3) NOYA.ai: Centered on "fund custody + strategy execution," abstracting prediction markets and DeFi execution into asset management products, providing a low-operation, low-mental-burden participation method. If it subsequently adds Prediction Market Intelligence and Agent execution modules, it is expected to form an integrated workflow of "research---execution---monitoring."

Compared to AgentFi projects like Giza and Almanak that 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 about $10M FDV, possessing significant valuation discounts and growth potential at this stage.

• NOYA: An asset management encapsulated AgentFi project centered on Omnichain Vaults, currently focusing on foundational infrastructure such as cross-chain execution and risk control, while upper-level Agent execution, prediction market capabilities, and ZKML-related mechanisms are still in development and validation stages.
• Giza: Capable of directly running asset management strategies (ARMA, Pulse), currently has the highest completion of AgentFi products.
• Almanak: Positioned as AI Quant for DeFi, outputting strategies and risk signals through models and quantitative frameworks, mainly targeting professional capital and strategy management needs, emphasizing the systematic nature of methodology and reproducibility of results.
• Theoriq: A strategy and execution framework centered on multi-agent collaboration (Agent Swarms), emphasizing a scalable agent collaboration system and mid- 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 "intent → multi-step on-chain operations" process, with users' perception of product value being relatively direct.

VIII. Conclusion: Business Logic, Engineering Implementation, and Potential Risks

Business Logic:

NOYA is a relatively rare subject in the current market, combining AI Agent × Prediction Market × ZKML multiple narratives, further integrating the product direction of intent-driven execution. In terms of asset pricing, it starts with an FDV of about $10M, significantly lower than the common valuation range of $75M--$100M for similar AI/DeFi/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) within the same execution framework, establishing a value capture closed loop through protocol revenue return (fees → buyback & burn). Although the project is still in its early stages, under the combined effects of multiple narratives and a low starting valuation, its risk-reward structure is closer to a high-odds, asymmetric game subject.

Engineering Implementation: In terms of verifiable delivery, NOYA's currently launched core functionality is Omnichain Vaults, providing cross-chain asset scheduling, yield strategy execution, and delayed settlement mechanisms, with relatively basic engineering implementation. The Prediction Market Intelligence (Copilot), NOYA AI Agent, and ZKML driven verifiable execution emphasized in its vision are still in development stages and have not yet formed a complete closed loop on the mainnet. At this stage, it is not a mature DeFi platform.

Potential Risks and Points of Attention

• Delivery Uncertainty: The technical leap from "basic Vault" to "fully functional Agent" is significant, and there is a risk of roadmap delays or ZKML landing not meeting expectations.
• Potential System Risks: Including contract security, cross-chain bridge failures, and unique oracle disputes in prediction markets (e.g., rule ambiguities leading to inability to adjudicate), any single point of failure could result in capital losses.

Disclaimer: This article was created with the assistance of AI tools such as ChatGPT-5.2, Gemini 3, and Claude Opus 4.5 during the writing process. The author has made efforts to proofread and ensure the information is true and accurate, but some omissions may still exist, for which we ask for your understanding. It should be particularly noted that the crypto asset market generally exhibits a divergence between project fundamentals and secondary market price performance. The content of this article is for information integration and academic/research exchange only and does not constitute any investment advice, nor should it be viewed as a recommendation for buying or selling any tokens.