Quack AI: A New Benchmark for AI Governance and RWA Compliance

Written by: Mario, Quack AI

“The introduction of Quack AI is establishing a clearer and more actionable framework for AI governance and RWA compliance in terms of participation mechanisms, decision-making quality, and execution paths. This not only marks a new stage in the maturation of decentralized governance but is also seen as the starting point of a governance revolution.”

Since the early days of the industry, DAOs have gradually become the mainstream governance method. However, we also see that, although the governance paradigm of DAOs is continuously iterating and evolving, this model has long faced a series of dilemmas such as low participation rates, slow governance, and security concerns.

In fact, the voting rate of most DAOs at this stage has long remained in the single digits, with Maker's participation as low as 2–3%. The governance of projects like Compound and Uniswap is often dominated by a few large holders, resulting in a high concentration of power. The low participation rate directly drags down governance efficiency. The processes of DAOs usually take several days or even weeks to complete, making it difficult to respond quickly to rapidly changing markets or security incidents. At the same time, the technical barriers and operational costs deter ordinary users, with high proposal thresholds, complex wallet interactions, and Gas costs invisibly excluding most token holders.

Of course, even when participating, the cognitive burden remains heavy. Whether it is the parameter adjustments of DeFi protocols or the funding allocation of community treasuries, proposals often involve complex financial or strategic considerations. Users lacking the assistance of tools find it difficult to understand and can only rely on the “core minority” to make decisions for them.

In addition, DAOs have long faced risks of manipulation and security. In 2022, Mango Markets was attacked due to price manipulation combined with governance voting, and Ooki DAO was pushed to the forefront of legal liability due to compliance issues. These cases illustrate that within the traditional paradigm, DAO governance struggles to avoid limitations such as lack of enthusiasm, distorted games, and even emotional decision-making.

Discussion on AI Governance

As AI gradually becomes the core narrative in the technology and cryptocurrency industry, discussions around AI governance are also heating up and are seen as a potential solution to the series of issues faced by DAOs.

From the characteristics of AI, it can process large-scale data, maintain stable high-frequency execution, and possesses capabilities in pattern recognition and risk assessment that surpass human abilities. Compared to a governance model that relies entirely on humans, the involvement of AI not only signifies an increase in efficiency but also represents a reconstruction of governance logic: humans remain responsible for value judgments and strategic direction, while data-intensive and easily manipulated aspects are entrusted to AI.

In fact, AI itself can take over a large number of tedious and high-frequency operations. For example, the model can automatically analyze on-chain data and community discussions, identify redundant or high-risk proposals, alleviate the information burden on users, and is expected to enhance learning and forecasting models to simulate different outcomes based on historical data, providing proactive risk alerts to help governance avoid emotional and shortsighted decisions. At the same time, automation driven by smart contracts can ensure that voting results are implemented immediately, reducing the vacuum period.

In terms of security, AI can continuously conduct risk monitoring and compliance audits, automatically identify abnormal voting and capital flows, and generate transparent and traceable governance reports, enhancing the fairness, compliance, and external interpretability of governance.

Therefore, AI governance is expected to address the long-standing dilemmas of DAOs, allowing humans to focus on value judgments and strategic choices, while delegating data-intensive, procedural, and easily manipulable aspects to machines, thus injecting new possibilities into decentralized governance.

On the other hand, as the global regulatory environment gradually becomes clearer, RWA (real-world assets) is becoming one of the mainstream narratives in the crypto market. Major countries around the world are exploring frameworks for tokenized assets, making compliance a baseline requirement. In this emerging market, which could reach trillions of dollars, information disclosure, compliance enforcement, and investor protection are gradually becoming top priorities, while the rapid advancement of tokenization has also raised unprecedented high standards for governance transparency and auditability.

Similarly, traditional on-chain governance tools find it difficult to directly meet these requirements: the voting mechanism of a DAO does not inherently support compliance disclosures, risk management, and compliance tracking across jurisdictions; relying solely on manual processes is both inefficient and prone to compliance loopholes. Therefore, how to leverage AI to rebuild trust mechanisms on-chain, and enhance the timeliness of disclosures, the proactivity of risk assessments, and the traceability of compliance audits through intelligent means is becoming the most critical issue.

In an industry that is still in the discussion stage of AI governance, Quack AI has taken the lead in practice. It has built a modular, native AI governance layer specifically designed for tokenized ecosystems, covering DAO, DeFi, and RWA. This framework enables end-to-end governance automation: from parsing disclosure documents, generating proposals, risk scoring, to executing voting and compliance audits. Quack AI provides the industry with a clear and actionable sample of AI governance.

Quack AI: A Universal Web3 AI Governance Layer Facility

Quack AI itself is a general Web3 AI governance infrastructure aimed at providing a better foundation for the entire tokenized ecosystem, particularly for scenarios like RWA. In this system, AI is embedded in the core governance processes: from information disclosure to proposal generation, from risk modeling to voting execution, and finally to compliance auditing and cross-chain implementation, forming an end-to-end automated closed loop.

Unlike traditional governance processes that rely on manual intervention, Quack AI is driven by data and intelligent agents at its core, ensuring that governance can be executed in real-time, is transparent and traceable, and maintains consistency in a cross-chain environment. It provides users with a low-friction way to participate, offers a scalable execution engine for protocols, and builds a trust foundation required for the tokenization of RWA with compliance and audit capabilities. As the industry is still in the exploratory stage, this framework has already shown the prototype of a “governance operating system,” providing an actionable standard for the integration of decentralized governance and real-world assets.

AI governance execution large model

Quack AI introduces an advanced artificial intelligence governance execution model that can further eliminate human inefficiencies in proposal evaluation, voting execution, and financial automation. Unlike traditional governance models that rely on static decision parameters, Quack AI continuously iterates and optimizes governance logic through machine learning, sentiment analysis, and on-chain behavior tracking, achieving more efficient and transparent governance execution.

The governance execution model is mainly composed of five key components:

AI Models and Scoring Engines: As the core of governance, they filter out noise in real-time, identify high-value proposals, and integrate on-chain behavior, user data, market events, and RWA metrics to generate credible governance scores.

AI Decision Logic: Embedded AI agents validate proposals throughout the entire process, assessing impact, risks, and compliance before execution, transitioning from passive voting to intelligent, autonomous decision-making.

Smart Contracts and Automation Engines: Governance results are automatically implemented through self-executing contracts, covering proposal storage, fund allocation, compliance verification, etc., ensuring that execution is transparent, secure, and consistent with ecological rules.

Cross-chain infrastructure layer: Supports cross-chain operations between public chains, L2, and RWA platforms, avoiding redundant deployments and ensuring that governance logic and execution remain consistent and interoperable in a multi-chain environment.

Privacy, Auditing, and Traceability Systems: Built-in privacy and auditing mechanisms ensure that all proposals and execution paths are traceable, while balancing transparency and data protection through selective privacy controls.

During execution, all governance decisions in the above model will go through a set of AI-driven analysis and verification processes before implementation.

Evaluation before proposal execution

The artificial intelligence governance agent first evaluates the quality and impact of proposals through neural networks, and identifies potential patterns by combining historical governance trends, filtering out redundant or low-value proposals from the source, before entering the emotion and data processing layer.

At the emotional and data processing level, AI utilizes natural language processing and sentiment analysis to extract real-time signals from community discussions, user feedback, and governance interactions. Based on this, proposals are sorted according to positive, neutral, or negative tendencies, ensuring that the governance direction aligns with community consensus.

Based on data insights, artificial intelligence decision algorithms continuously adjust governance parameters through reinforcement learning, and utilize predictive models to optimize proposal selection, proactively avoiding potential risks and enhancing decision-making foresight. Meanwhile, data validation and anomaly detection mechanisms cross-reference proposals with on-chain transaction history, equity distribution, and past governance records, using anomaly detection models to identify manipulative or malicious behavior, thereby ensuring fairness and transparency in governance.

Ultimately, all proposals that pass the screening and optimization will enter the on-chain smart contract automation module.

Execution Phase

Based on the on-chain smart contract automation module, governance will directly interact with AI agents and smart contracts, achieving full process automation from voting to fund management. The design of this module is not just an execution tool, but also a governance execution system that continuously learns and optimizes.

The on-chain smart contract automation module includes several main components, such as governance proposal contracts, smart governance contracts, fund management contracts, and compliance and security contracts.

During the initial stage of governance execution, the governance proposal contract will first store the proposals evaluated by AI on the blockchain and transparently execute voting transactions on behalf of users. It will automatically reject invalid or duplicate proposals, ensuring the efficiency and order of the governance process from the source.

Furthermore, Quack AI itself supports cross-chain user participation through a set of AI delegation frameworks. Users can delegate governance rights to real-time AI agents (such as Sentinel focused on risk-aware voting, and Agora focused on optimizing proposals towards a more community-friendly direction) to execute these rights. These agents will make voting decisions based on the parameters set by users, allowing governance participation to be maintained even when users themselves are inactive.

To prevent excessive concentration of power, the system has also designed a dynamic voting weight calibration mechanism that continuously adjusts the delegation weight based on users' historical behavior, staking situation, and trust scores, effectively curbing centralization while ensuring fairness.

When governance decisions are reached, Quack AI's agents autonomously execute the results directly on the supported blockchain. This not only eliminates the common delays and operational omissions found in manual governance but also allows approved proposals to be executed in real time, avoiding a vacuum at the execution level. Even ordinary users who do not have the capacity for continuous manual participation can remain active by delegating to AI agents, ensuring that their influence is reflected, thereby achieving a true sense of “continuous participation, zero friction.”

Beyond governance, Quack AI extends the capabilities of autonomous organizations to the financial level by integrating AI-driven financial automation, achieving risk optimization and tamper-proofing, enabling governance to extend throughout the entire process of financial execution and incentive distribution.

Based on this, Quack AI also offers multiple levels of financial execution methods:

It supports automatic income sharing, allowing blockchains integrated with Quack AI to set different profit distribution mechanisms according to their governance needs;

AI is capable of directly executing governance-based fiscal allocations, completing fund distribution, equity rewards, and incentive measures.

The system can also perform intelligent assessments of proposal-driven funding requests and determine the best grant scheme by combining historical performance and impact analysis.

At the same time, Quack AI is further avoiding risks through a institutionalized execution framework:

Multi-layer compliance verification: Before execution, the system will check whether the proposal has been approved by verified governance participants, whether compliance and jurisdictional conditions are met, and whether there are risk warnings or logical conflicts.

Triggerable external supervision: Any anomalies can trigger manual review or multi-agent consensus, preventing AI from being “overstepped” or exploited at a single point;

Open multi-model mechanism: allows external models and agents to access and execute on the market, creating diverse competition and checks and balances, rather than hardcoding a single LLM;

Transparent and Auditable: All fund flows output standardized logs, which can be independently replayed and verified by third parties or the community.

Through the above mechanisms, Quack AI not only eliminates inefficiencies and human biases in financial decision-making but also avoids the single point of risk that may arise from “naive AI governance.” Similarly, governance results can be executed instantly and securely while maintaining institutional diversity and oversight, ensuring that autonomous organizations remain compliant and scalable when facing complex scenarios such as DeFi and RWA.

In the final link of the governance process, compliance and security contracts play a dual role of protection and auditing.

This contract incorporates an anti-manipulation mechanism that actively identifies and prevents potential governance attacks, ensuring that the system is not interfered with by malicious actions during execution. To preemptively address risks, the AI will perform verification and review at the proposal stage, automatically filtering out spam, malicious proposals, and suspicious voting manipulation strategies.

At the same time, the system will generate governance audits and transparency reports, detailing voting behavior, fund allocation, and decision-making logic, providing clear and traceable evidence for the community and regulators. In addition, Quack AI relies on an AI-driven fraud detection mechanism to monitor governance transaction flows in real-time, detecting and stopping potential attacks at the first opportunity, thereby ensuring that the entire governance process operates within a fair, transparent, and compliant framework.

Therefore, through the aforementioned system, Quack AI can not only optimize the allocation of voting rights but also automatically complete the implementation of proposals, fund disbursement, and incentive distribution, allowing governance results to be executed in real-time, transparently, and securely, thereby truly achieving the immediacy and credibility of governance.

AI is the engine, and humanity is the steering wheel.

Ethereum founder Vitalik Buterin once published an article titled “AI as the engine, humans as the steering wheel” on his blog, stating: “A single AI system directly responsible for governance or fund allocation can be easily exploited, and more robust governance can be achieved through open, diverse, and auditable institutional design.” This aligns with the philosophy of Quack AI.

In the governance framework of Quack AI, AI serves as the execution layer with the aim of complementing human beings. Its model can be summarized as “AI is the engine, and humans are the steering wheel,” meaning AI is responsible for data processing, trend forecasting, and execution, while humans are responsible for setting value goals and strategic direction.

In order to achieve this goal, Quack AI introduced a series of mechanisms including:

Distilled Human Judgment (DHJ) introduces a decentralized jury to provide ethical and strategic references for AI model training, preventing it from becoming a black box decision-maker.

The Futarchy model also combines prediction markets with community voting, allowing AI to optimize governance paths under the overall goals set by the community, ensuring alignment with the long-term vision.

In terms of funding allocation, the AI-enhanced grant mechanism will take into account impact, feasibility, and historical performance, with human validators overseeing key indicators, while AI precisely executes the allocation, reducing bias and waste.

In the content ecosystem, AI-driven content filtering works in conjunction with human committee oversight, ensuring both the efficiency and value of the information flow, while avoiding distortion and manipulation.

Through this entire design, Quack AI not only leverages the advantages of artificial intelligence in efficiency and accuracy, but also retains human dominance over moral and strategic direction, thus constructing an efficient, trustworthy, and transparent AI-enhanced decentralized governance paradigm.

Source of the image:

Multi-chain Governance

The governance model of Quack AI itself has cross-chain characteristics, aiming to function simultaneously across multiple blockchain ecosystems, allowing users to participate in governance across chains and promote decision execution.

The core lies in building an artificial intelligence governance interoperability layer, where AI will track governance trends across different blockchains in real time, optimizing cross-chain voting logic, so that governance insights from one chain can directly influence governance actions on another chain.

Currently, Quack AI is not only compatible with Ethereum's native governance mechanism, but it also provides a governance report API for EVM protocols, enabling direct interaction with Quack AI's analytical results. It is worth mentioning that Quack AI has already been implemented in over 50 ecosystems, each of which incorporates built-in AI agents, real-time execution, and risk-aware decision-making models, ensuring governance across ecosystems is collaborative, transparent, and smooth.

Based on its cross-chain toolkit, Quack AI has launched the first cross-chain AI governance center, supporting communities, DAOs, and institutions to interact in real-time with AI-driven governance. It is not only used for participation but also ensures automatic execution of decisions, risk-aware voting, and financial execution, avoiding human bottlenecks.

Empowering RWA Governance

With the rapid expansion of tokenized assets, how to build a sustainable institutional framework on-chain that covers the entire chain from asset monitoring to compliance execution is becoming a new industry pain point. Quack AI addresses this pain point by providing a governance module specifically designed for RWA, helping platforms achieve automated, compliant, and traceable governance throughout the entire asset lifecycle.

The starting point of governance lies in asset monitoring.

Quack AI can track the changes in Net Asset Value (NAV) in real time from oracle and off-chain data sources. When there is an abnormal market fluctuation, the system will immediately generate rebalancing or unlocking proposals, incorporating the risks into the governance process. Connected to this is the management of the redemption queue; when redemption pressure approaches the limit, the AI agent will automatically trigger freeze or delay logic to avoid bank run risks and support the governance-level restructuring of the redemption structure.

To ensure that assets can be reliably mapped to the blockchain, Quack AI has introduced Proof of Reserves (PoR). This mechanism continuously verifies the timestamps and validity of submitted proofs, automatically flags expired or invalid data, and updates or suspends proposals when necessary, ensuring consistency between on-chain and real-world assets.

On the compliance level, Quack AI introduces an identity threshold governance system, in which voting rights are tied to verified identities and equity ratios, combined with KYC/AML gating and jurisdiction filtering, achieving differentiated governance for cross-regional compliance, allowing on-chain decisions to truly connect to real-world regulatory frameworks.

In addition, RWA governance needs to have event response capabilities. The asset event trigger module of Quack AI can convert significant events in legal, financial, or operational contexts into on-chain governance signals, enabling governance to feature real-time perception and automatic response.

Through the interconnected mechanisms mentioned above, Quack AI is building a complete closed-loop governance system for the RWA platform that covers monitoring, risk, compliance, execution, and response. This allows tokenized funds, bonds, equity, and other assets to operate safely and transparently on the blockchain, while also providing a credible institutional foundation for the large-scale on-chainization of real-world asset markets.

Ecological Role

Currently, the governance system of Quack AI mainly includes two types of roles: one is community users who participate in governance, and the other is developers and third-party dApps on the B-side.

Users participating in governance

To participate in governance through the AI governance layer, you need to hold Passport assets to obtain an on-chain identity. This asset serves as a gas fee-based credential, which can act as the user's on-chain identity within the Quack AI governance layer. Holders can use this asset to delegate their voting rights to AI agents, receive governance airdrops, track participation metrics, and access rewards.

After users delegate their votes, they will no longer need to vote manually. The AI agent will call on on-chain data, historical governance models, and community sentiment to evaluate each proposal and automatically generate rankings and priorities. Users can gain insights provided by AI before voting or delegating, shifting governance from “intuition-based” to “data-driven”. Furthermore, these agents will autonomously complete voting based on the logic and behavior patterns preset by users, executing immediately after a proposal passes, eliminating any vacuum caused by human delays or operational omissions. At the same time, users still retain overriding rights and can intervene manually on critical issues at any time.

The system will track users' participation and authorization behavior, dynamically adjusting reward distribution based on activity level, historical contributions, and voting quality, making incentives fairer and more transparent. As of now, over 3 million Passport users have participated in the Quack AI governance module, validating the effectiveness of this model.

Developer community

For developers, Quack AI is a modular AI governance layer that supports cross-chain end-to-end decision automation, execution, and risk-aware coordination.

Builders and developers can integrate Quack AI into dApps, protocols, or ecosystems to unlock AI-generated proposal insights, delegated voting mechanisms, autonomous workflow execution, real-time governance analysis, as well as on-chain rewards and financial automation, thereby alleviating governance burdens and enabling smart, tamper-proof decision-making.

Currently, more than 10 chains and over 40 on-chain protocols have adopted the governance framework of Quack AI, and are deeply integrated with BNB Chain, Arbitrum One, Optimism, Polygon, Avalanche, Base Chain, Linea, Metis Chain, Taiko, Monad Testnet, Merlin Chain, Berachain, HashKey Chain, DuckChain, etc., aiming to expand across ecosystems rather than being limited to a single link.

Developers can access the AI governance data API to obtain proposal data, governance analysis, and AI-generated insights in real time, monitoring cross-chain governance trends. They can also invoke AI-driven governance monitoring and reporting, retrieve governance activity logs, proposal outcomes, and participation metrics, and utilize sentiment analysis reports and trend prediction models to assist in decision-making. Through smart contracts and financial governance analysis, developers can access AI-optimized fund management reports, track token distribution and equity allocation, and leverage automated compliance monitoring to ensure all decisions comply with governance policies.

According to Quack AI's plan, a complete API suite for developers is being gradually rolled out, opening governance data, voting logs, proposal scoring, and AI models, allowing developers to integrate Quack AI's governance engine into external applications and dashboards.

In the future, Quack AI will also launch an AI Governance SDK, supporting dApps to directly integrate automatic decision-making execution; at the same time, it will provide an automated smart contract API, allowing DAOs to fully automate proposal processing, voting, and execution across multiple chains; and promote multi-chain governance execution on Ethereum and other networks through governance orchestration tools. By accessing Quack AI's API and analytical tools, developers will be able to enhance application functionalities with AI-driven governance intelligence while ensuring that Quack AI always functions as an autonomous, scalable, and cross-chain compatible governance protocol.

RWA Issuer

For RWA issuers, Quack AI has proposed a modular governance system specifically designed for RWA to provide a clear and operable compliance hub for on-chain real assets.

The system is capable of real-time tracking of key signals such as NAV fluctuations, redemption pressure, PoR data expiration, and liquidity thresholds, and generates on-chain audit logs to meet regulatory requirements for “verifiable and interpretable” data. In terms of compliance and identity, Quack AI ensures that governance participants meet qualified investor standards and comply with cross-regional regulatory requirements through KYC/AML gating and jurisdictional filtering, thereby truly empowering RWA issuers.

Therefore, for institutions, this means that they do not have to be forced to adapt to a completely new governance paradigm. Traditional decision-making processes such as boards of directors and shareholders meetings can be smoothly migrated on-chain and directly connected to compliance modules and AI execution layers. Whether it is tokenized funds, bonds and equity platforms, or financial-grade underlying chains and other permissioned chains, they can also unify compliance, automation, and cross-chain execution into the same governance layer with the help of Quack AI.

With this system, Quack AI will assist RWA in further achieving a complete closed loop from asset monitoring, compliance identity, risk control, to institutional landing. This not only addresses the core challenge of “how to govern after assets are on-chain,” but also provides a reliable governance and compliance standard for the real asset market, which amounts to trillions of dollars.

A New Starting Point for the Governance Revolution in Web3

Overall, Quack AI's entry point is very precise. It embeds intelligent agents in the stages of proposals, voting, and execution, allowing the most labor- and time-consuming phases to be handled by machines, truly advancing the operational logic of DAOs from “formal autonomy” to “usable autonomy.”

This model allows humans to focus their judgment on value trade-offs and strategic directions, while delegating process execution and result optimization to machines, thereby significantly reducing governance friction and enhancing the transparency and operability of governance.

At the same time, the scaling of RWA onto the blockchain is becoming one of the most important incremental narratives in the industry. Quack AI is making the certification and circulation of RWA more efficient and trustworthy, while also providing verifiable transparent trails for financial institutions and compliant entities, ensuring that large-scale onboarding to the blockchain has institutional and regulatory safeguards.

Therefore, the paradigm of Quack AI can be regarded as an innovation in DAO tools, which not only promotes the maturation of the governance system itself but also provides an institutional foundation for the reconstruction of on-chain financial order and the large-scale implementation of RWA.

Based on Quack AI, in the future, AI governance will become a key engine driving the dual evolution of on-chain governance and asset tokenization, marking a new starting point for the governance revolution.