Foundational Paradigm — The Dual Revolutions of AI and Web3

The Rise of AI Agents: From Tools to Economic Actors

We are standing at the threshold of a profound transformation: artificial intelligence (AI) is evolving from a mere assistive tool into autonomous entities — agents — capable of perceiving, reasoning, making decisions, and interacting with both the physical and digital worlds. This shift is not just a technological leap; it signals the emergence of a brand-new economic participant. Once AI agents achieve economic independence, a trillion-dollar, machine-driven, high-speed “agent economy” will no longer be science fiction — it will be imminent reality.

1.Defining AI Agents: From Automated Scripts to Autonomous Entities

The evolution of AI agents clearly demonstrates leaps in intelligence. Early automation scripts rigidly followed predefined rules, passively executing tasks with no adaptability or learning. With the breakthrough of large language models (LLMs), however, modern AI agents now possess unprecedented autonomy.

The defining traits of a modern AI agent include:

• Autonomy: Operating without continuous human intervention — able to set sub-goals, make decisions, and act to achieve final objectives.

• Perception & Interaction: Sensing their digital environment (e.g., API responses, web content, on-chain data) and using tools (e.g., code executors, browser extensions) to interact with it.

• Reasoning & Planning: Using perceived data and long-term goals to conduct complex reasoning, breaking down broad objectives into executable steps.

• Adaptability: Learning from outcomes, dynamically adjusting strategies to handle environmental changes or unexpected errors.

According to Deloitte, by 2027, half of all companies deploying generative AI will be piloting AI agents. The market is expected to surge from $5.1 billion in 2024 to $47.1 billion in 2030. This exponential growth reflects not only the technology’s maturity but also the surging demand for agents as a new class of productive force.

2. Economic Independence: The Ultimate Form of AI Agents

If autonomy is the “mind” of an AI agent, then economic independence is its “body and lifeblood” in the real world. Gaining economic independence is what turns an AI agent from a digital “player” in simulations into a true “economic actor” in open systems.

Economic independence means more than just executing tasks — it implies that an AI agent can own and manage assets (e.g., digital wallets), earn income by providing services or creating value, cover its operating costs (e.g., API fees, compute expenses), and ultimately participate in an open, closed-loop economy.

Stablecoins are widely regarded as the financial bedrock for this vision. They enable AI agents to transfer value globally in real time and with predictability. Unlike highly volatile cryptocurrencies, stablecoins provide a unified unit of account, greatly reducing the complexity of machine-to-machine (M2M) economic transactions. From Google’s open-source payment protocol to Coinbase’s x402 mechanism, stablecoins are the centerpiece of value exchange for AI agents. Galaxy Digital CEO Mike Novogratz has even predicted that AI agents will become the largest users of stablecoins. This points to a looming era of high-frequency, automated microtransactions — an economy powered by AI agents and fueled by stablecoins.

3. Technical Foundations: The Core Engines of Autonomous Agents

The capabilities of modern AI agents stem from their modular architecture, composed of key components:

• Brain – The LLM Core: Acting as the cognitive center, LLMs interpret natural language instructions, conduct commonsense reasoning, and generate action plans.

• Perception Module: Via APIs, web crawlers, and on-chain data indexers, agents can “see” and “hear” the digital world.

• Planning Module: Decomposing high-level goals into orderly, executable subtasks. For example, “find me the highest-yield DeFi strategy” may be broken down into: “search leading protocols → fetch APY data → compare risk parameters → propose optimal solution.”

• Action Module: Using a suite of tools (e.g., code interpreters, API callers, smart contract interaction modules) to execute planned steps, thereby affecting the external world.

The synergy of these modules elevates AI agents from passive executors of instructions into proactive explorers and value creators. This solid technical foundation underpins their emergence as independent economic actors.

The Shift Toward Intention-Centric Web3

While AI agents are poised to take off, the Web3 ecosystem itself is undergoing a deep paradigm shift — from being transaction-centric to becoming intention-centric. This transformation aims to address the long-standing user-experience gap that has hindered Web3 adoption, and in doing so, it lays the groundwork for large-scale AI agent deployment.

1. The Current Dilemma: The UX Barrier to Mass Adoption

Despite Web3’s grand vision of decentralization and user sovereignty, its poor user experience has remained its Achilles’ heel. For mainstream users, interacting with decentralized applications (dApps) is riddled with obstacles:

• A steep learning curve: Users must grapple with complex technical concepts such as wallets, private keys, gas fees, and transaction signatures.

• Cumbersome workflows: A seemingly simple cross-chain swap might require users to manually connect wallets, approve permissions, sign transactions, and bridge assets across multiple interfaces.

• Hidden economic risks: Users bear the costs of MEV (Maximal Extractable Value) attacks in the form of slippage, and face failed transactions where gas fees are still consumed.

This paradigm, which effectively demands that users become “technical experts,” excludes most people without a technical background. Current Web3 interaction logic still asks users to specify how to perform each step, instead of simply stating what outcome they want.

2. Defining Intention-Centric Architecture: From “How To Do” to “What You Want”

Intention-centric (intent-centric) architecture emerges as the antidote. It is a design philosophy that places the user’s end goal at the center, abstracting away the executional complexity entirely.

In this model, users no longer construct and sign specific transaction instructions. Instead, they sign a message expressing their desired end state — an “intent.” For example: “I want to swap up to 100 USDC in my account for as much ETH as possible, provided slippage does not exceed 0.5%.”

This seemingly small shift moves Web3 interaction from a command-based to a declarative paradigm. Users simply declare what they want, while the burden of figuring out how it should be done is delegated to a competitive network of specialized, decentralized participants.

3. Technical Breakdown: Intents, Solvers, Intent Pools, and the Settlement Layer

The practical realization of an intent-centric system relies on several interlocking components:

1. Intent: Users express their goals through user-friendly front ends (e.g., natural language inputs) and sign structured intent data.

2. Intent Pool: The signed intent is broadcast to a public space — on-chain or off-chain — similar to a mempool, where it awaits processing.

3. Solvers: A set of specialized, competing off-chain actors (humans, algorithms, or AI agents) monitor the intent pool. They search for optimal execution paths — for example, sourcing the best liquidity or bundling transactions to save gas — in order to fulfill user intents.

4. Settlement Layer: Solvers package their chosen solution into one or more concrete transactions, which are then submitted to the underlying blockchain (e.g., Ethereum) for final settlement. From the user’s perspective, they simply see their wallet balance update — without ever worrying about the complex steps in between.

In this way, intent-centric architecture elegantly resolves many of Web3’s longstanding challenges. It dramatically simplifies user experience, while solver competition delivers better economic outcomes (e.g., MEV protection, improved pricing). Most importantly, it creates the ideal proving ground for AI agents — who, as the ultimate “solvers,” can thrive in such an ecosystem.

Synergistic Fusion — The Core Mechanism of a New Economic Paradigm

This section forms the heart of the report. It explores the synergy between two paradigms — AI agents and intent-centric Web3 — and shows how their convergence gives rise to an entirely new, autonomous, machine-to-machine economy.

Perfect Symbiosis: AI Agents as the Intelligent Engine of Intent Systems

The fusion of intent-centric architecture with AI agents marks a profound paradigm shift within Web3. In this combined model, AI agents are no longer just tools; they become the intelligent, efficient executors within intent-based systems. Each empowers the other, together building a more resilient and intelligent digital ecosystem.

1. The Ultimate Solver: How AI Agents Parse and Execute Complex Intents

AI agents — especially those built on large language models (LLMs) — possess exceptional natural language understanding, reasoning, and planning capabilities, making them ideally suited for the role of solver in intent-centric systems.

Users often express their goals in vague, high-level terms such as “I want to maximize my ETH yield in DeFi.” Traditional blockchain systems would require the user to manually execute a series of complex on-chain actions to achieve this. By contrast, AI agents can transform such imprecise requests into optimized sequences of on-chain operations.

As the ultimate solvers in intent systems, AI agents can:

• Understand and interpret: Using LLMs, they deeply parse user intents across multiple protocols and chains, even when intents involve multi-step, multi-condition “declarative commitments.”

• Plan and decide: After understanding an intent, they use planning modules — drawing on real-time on-chain data such as liquidity, gas fees, and slippage — to chart the optimal execution path. This may involve aggregating fragmented liquidity across DEXs, leveraging flash loans to maximize efficiency, or dynamically adjusting DeFi strategies in response to market shifts.

• Optimize and execute: Through their action modules (e.g., smart contract interactions, transaction management), AI agents convert planned paths into concrete blockchain transactions. They can automate wallet interactions, signatures, and gas management, ensuring the user’s intent is fulfilled with maximum efficiency and cost-effectiveness. For example, within CoW Protocol’s batch auction system, AI-driven solvers can competitively search for “coincidences of wants” to deliver MEV protection and improved pricing.

This ability frees users from the burden of how to act, allowing them to focus purely on what they want. In doing so, AI agents lower the barrier to entry for Web3 and significantly boost efficiency.

2. Empowering Agents: Intention Architecture as a Safe Sandbox

Intent-centric architecture not only relies on AI agents but also provides them with a controlled operating environment — a kind of “safe sandbox.” This reciprocal empowerment is critical to ensuring that AI agents can operate safely and reliably in economic contexts.

• Boundaries and constraints: User intents are expressed with explicit, verifiable constraints (e.g., max slippage, minimum yield, transaction deadline). The architecture ensures all agent actions must respect these boundaries. Any solution that violates them is automatically rejected.

• Competitive validation and minimized trust: Because multiple solvers (including AI agents) compete to satisfy intents, no single agent is trusted absolutely. Competition ensures efficiency and price optimization, while settlement-layer verification or cross-validation among solvers provides additional safeguards. Malicious or faulty solutions are naturally filtered out.

• Auditability and accountability: Each execution by an AI agent is tied to a clear, signed user intent. This creates a transparent behavioral trail. If outcomes deviate from expectations, the system can trace whether the fault lay in intent specification or in agent execution — making responsibility clear.

Through this trust-minimized sandbox, intent-centric architecture reduces the risks of agent misbehavior or unintended consequences — laying a safety foundation for deploying AI agents at scale in sensitive domains like finance.

3. The Data Flywheel: Agents, Data Supply, and the Learning Loop in DeFi

The convergence of AI agents with intent-centric systems creates a powerful data flywheel — especially evident in DeFi. Agents consume vast amounts of data to make decisions, while each action they take generates new data, feeding back into continuous learning and optimization.

• Data-driven decisions: To manage liquidity, arbitrage, assess risk, and execute DeFi strategies, agents must constantly analyze vast datasets: token prices, liquidity depths, lending rates, transaction volumes, smart contract events, MEV activity, and even macroeconomic indicators. Oracle networks like Pyth provide ultra-low-latency, reliable first-party data as critical inputs for these decisions.

• Execution generates new data: Every transaction, strategy adjustment, and position management step performed by agents produces additional on-chain data: trade records, fund flows, and interaction histories. This fresh data then becomes the basis for further learning.

• Continuous optimization and evolution: By ingesting data, executing strategies, and analyzing feedback, AI agents can iteratively refine their models. They may recalibrate risk parameters, detect hidden arbitrage opportunities, or develop more efficient liquidity provisioning strategies. Allora Network, for instance, incentivizes high-quality AI predictions by connecting data providers, processors, and users — a prime example of the data flywheel in action.

This self-reinforcing loop enables AI agents to adapt to the ever-shifting Web3 environment, continuously enhancing intelligence and economic performance. Over time, it forms an evolving, self-optimizing intelligent economic ecosystem.

Digital Blood: Stablecoins as the Lifeblood of the M2M Economy

As AI agents become increasingly autonomous and deeply embedded in economic activity, the role of stablecoins becomes indispensable. They are not only the bridge between traditional finance and decentralized systems but also the circulatory system that keeps the machine-to-machine (M2M) economy running efficiently.

1. Why Stablecoins? The Machine Economy’s Demand for a Unit of Value

While native cryptocurrencies like ETH and BTC offer decentralization and permissionless access, their inherent volatility makes them unsuitable for high-frequency, low-value, stability-dependent machine-to-machine transactions. If the underlying asset fluctuates wildly, automated payments, settlements, or strategy executions by AI agents would become risky and unreliable.

• Anchored value, reduced volatility: By pegging to fiat currencies like the U.S. dollar or to other stable assets, stablecoins provide a predictable value anchor. This allows agents to budget, plan, and execute financial strategies with consistency, avoiding the uncertainty of volatile assets.

• A unified unit of account: In a multi-protocol, cross-chain economy, stablecoins serve as a common unit of measure and medium of exchange. This greatly simplifies accounting and settlement between AI agents, reducing system complexity.

• Predictable costs and returns: For agents engaged in arbitrage, liquidity provision, or other profit-driven strategies, stablecoins ensure that both costs and outcomes can be forecasted with confidence — enhancing efficiency and reliability.

Because of this unique stability and predictability, stablecoins have become the financial foundation for translating the concept of an AI agent economy into practice. Galaxy Digital CEO Mike Novogratz has even forecast that AI agents will soon become the largest consumers of stablecoins, underscoring the enormous potential of the M2M economy.

2. Programmable Value Flows: Payments and Settlement at Machine Speed

Stablecoins are more than just stable digital assets — they are programmable money. When paired with AI agents’ automation, they unlock payment and settlement at “machine speed,” creating an entirely new economic paradigm.

• Automated and conditional payments: Via smart contracts, stablecoins can be programmed for conditional, automated transfers. For example, an AI agent could be coded to release payment only once a service is delivered, data verified, or a DeFi position reaches a set threshold. This “payment upon delivery” logic can be executed trustlessly and without human intervention, boosting both efficiency and reliability.

• Instant settlement and micropayments: With blockchains enabling “payment as settlement,” transactions finalize within seconds or minutes — far faster than traditional banking systems. This is critical for M2M scenarios requiring frequent micropayments, such as agents paying for API calls, compute cycles, or streaming small packets of data.

• Global, frictionless transfer of value: Stablecoins move seamlessly across borders and time zones, 24/7, usually at lower cost than traditional remittance systems. This enables AI agents worldwide to interconnect into a global economic web, facilitating frictionless value exchange at scale.

These programmable, high-speed value flows are the lifeblood of an agent economy, allowing AI agents to act as true economic actors — transacting autonomously in the global digital economy.

3. Key Payment Protocols and Standards (AP2, x402)

To ensure that AI agents can use stablecoins safely and seamlessly, the industry is developing a range of key protocols and standards. These initiatives aim to bridge Web2 and Web3 while giving AI agents unified interfaces for value exchange.

• Google Agent Payments Protocol (AP2): Google’s AP2 builds on its earlier Agent-to-Agent (A2A) and Model Context Protocol (MCP) work, providing a standardized framework for agent-to-agent value exchange. Natively supporting stablecoins and crypto, AP2 introduces mandates — verifiable digital contracts that translate user intents into authorized financial actions. This resolves trust issues around authorization, authenticity, and accountability in agent-driven transactions.

• Coinbase x402 protocol: In collaboration with Google and others, Coinbase has advanced the x402 mechanism, leveraging HTTP’s “402 Payment Required” status code. This allows AI agents to settle micropayments directly in stablecoins (like USDC) during network interactions — effectively embedding payments into APIs, applications, and agent communications. Seen as a breakthrough for agent monetization, x402 significantly increases the efficiency of M2M commerce.

• Ethereum Foundation and ERC-8004: The Ethereum Foundation is exploring trust-agent standards such as ERC-8004 to strengthen Ethereum’s role as a settlement and coordination layer for AI economies. These efforts aim to create a unified framework for value transfer and agent interaction, fostering an open, censorship-resistant decentralized AI ecosystem.

Together, these protocols and standards signal the growing maturity of AI agent infrastructure. They not only provide the technical feasibility for stablecoin-powered economies but also lay the foundation for large-scale adoption within Web3.

On-Chain Soul: ERC-6551 and the Digital Identity & Ownership of Agents

For AI agents, economic independence requires more than just stable currency and efficient payment rails. At a deeper level, they need an identity on-chain — one that can own assets and be recognized as a distinct participant. ERC-6551, also known as Token-Bound Accounts (TBAs), provides exactly this: the “on-chain soul” that upgrades agents from mere code to blockchain-native entities with digital identity and ownership rights.

1. From Code to Identity: NFTs as Unique Agent Credentials

In Web3, NFTs — thanks to their uniqueness, immutability, and programmability — naturally serve as identifiers for AI agents.

• Uniqueness & immutability: Each NFT is one-of-a-kind, with ownership recorded immutably on-chain. This gives agents a permanent, tamper-proof identity, solving the problem of authentication and trust in decentralized environments.

• Programmable metadata: Beyond identity, NFTs can store rich attributes: the agent’s name, description, permission levels, version info, or even the hash of its core algorithms. These can evolve over time as the agent upgrades, making the identity flexible and expressive.

• Transferability & marketization: An agent’s NFT identity can be bought, sold, or transferred, enabling markets for agent ownership. This opens new business models — such as Agents-as-a-Service — and creates novel financing opportunities.

By anchoring AI agents to ERC-721 NFTs, abstract lines of code become tangible, recognizable digital individuals within the Web3 economy.

2. ERC-6551: Giving Agents Wallets and an On-Chain “Soul”

ERC-6551 is a groundbreaking Ethereum standard that allows every ERC-721 NFT to own an independent smart contract account — essentially a wallet. For AI agents, this is transformative: it grants them an on-chain “soul.”

• Asset ownership: With an ERC-6551 account, an NFT representing an AI agent can directly hold tokens (including stablecoins like USDC or USDT), other NFTs, and even receive or send funds. Agents no longer need external EOAs (externally owned accounts) or multisigs — they can own and control assets directly.

• Autonomous dApp interaction: As smart contract accounts, TBAs can natively interact with DeFi, GameFi, DAOs, and other Web3 applications. Agents can be programmed to conduct liquidity mining, lending, swaps, or governance voting entirely on their own.

• Programmable, trustless economic activity: With their own wallets and interaction capabilities, agents can fully automate on-chain economic strategies. For instance, an NFT-represented trading agent could manage its portfolio, execute arbitrage trades, collect profits directly into its TBA, and reinvest or pay API fees — without any human intervention.

By equipping agents with TBAs, ERC-6551 transforms them from passive tools into blockchain-native entities with “digital bodies” and independent economic capabilities. AI projects such as CharacterX are already leveraging this standard to design sustainable economic models for agents. Base founder Jesse Pollak has even hinted at its potential in powering Elon Musk’s AI chatbot Grok — underscoring market confidence in ERC-6551’s role at the AI–crypto frontier.

3. On-Chain Reputation: Building Verifiable Agent Histories

With ERC-6551 granting agents independent accounts, all their economic activities are permanently recorded on-chain. This creates a verifiable history and reputation system — critical for enabling agents to engage in more advanced economic activity.

• Traceable transaction history: Every trade, smart contract interaction, and fund flow performed through an agent’s TBA is transparently recorded, forming a complete “digital resume” of its economic behavior.

• Behavior-based reputation: Third-party protocols or DAOs can analyze an agent’s account history to score its trustworthiness. An agent that reliably repays loans, executes profitable arbitrage, or actively participates in governance can accumulate a strong reputation.

• Unlocking advanced activities: High-reputation agents could qualify for unsecured loans, participate in advanced DeFi strategies (e.g., flash loans), or gain access to more lucrative decentralized marketplaces — mirroring how human credit scores unlock financial privileges.

• Fostering collaboration & M2M trust: Trust is the foundation of agent-to-agent collaboration. Reputation systems provide an objective way for agents to evaluate each other, enabling more complex networks of cooperation in the M2M economy.

By solving identity, ownership, and reputation all at once, ERC-6551 gives AI agents the infrastructure to operate as sovereign, trusted economic participants in a decentralized Web3 world.

Ecosystem Tour — Market Landscape and Case Studies

Pioneers of the Intent Paradigm: In-Depth Case Studies

Intent-centric architecture has emerged as a key paradigm in Web3, designed to simplify user experience and optimize transaction execution. This chapter analyzes three representative projects — Anoma, CoW Swap, and SUAVE — to show how intent-driven design addresses pain points in today’s Web3 ecosystem.

1. Anoma: The Grand Vision of a Universal Intent Machine

Anoma is regarded as the pioneer of intent-centric architecture. Its vision is to build a universal intent machine — a full-stack decentralized application architecture. Anoma seeks to solve fundamental blockchain challenges such as counterparty discovery, privacy protection, and multi-chain settlement.

By introducing components like an intent propagation layer, programmable solvers, and programmable threshold decryption, Anoma provides infrastructure for complex dApps. Its core principle is simple: users only need to express their desired end state (intent), and the system automatically finds and executes the optimal path to achieve it.

2. CoW Swap: A Practitioner Focused on Trading Intents and MEV Protection

CoW Protocol (CoW Swap) is an intent-driven decentralized exchange (DEX) dedicated to trading intents and MEV (Maximal Extractable Value) protection.

It uses a batch auction mechanism to aggregate user intents, which are then competitively fulfilled by a solver network. CoW Swap not only finds users the lowest execution price but also directly matches “coincidences of wants” (CoW), preventing MEV attacks.

Its CoW Hooks feature lets developers and advanced traders program custom logic to better realize trading intents. To date, CoW Protocol has processed over $1 billion in transaction volume and generated substantial profits.

3. SUAVE: The Future of Decentralized Ordering and the MEV Market

SUAVE (Simple Universal Auction for Value Expression), developed by Flashbots, is a modular, standalone, pluggable MEV-aware ordering layer. Its mission is to fully decentralize block building.

Its core concept, Preferences (analogous to “intents”), allows users to customize how their transactions are handled. By combining off-chain preference expression with trusted execution environments (TEE Kettles), SUAVE preserves privacy while enabling high-performance computation.

SUAVE processes intents originating from external blockchains and provides cross-chain settlement to mitigate MEV-related risks within EVM systems. Ultimately, it aims to create a fair, transparent, and efficient decentralized transaction processing infrastructure. Its testnet, Toliman, is already open to developers for building intent systems and AI-agent tooling.

The Rise of Agent Platforms: Competition Among Ecosystem Builders

As the agent economy takes shape, a range of platforms are emerging to provide the infrastructure for an AI-agent-driven ecosystem.

1. Crypto-Native Forces: Fetch.ai, SingularityNET, Bittensor

These crypto-native projects are creating decentralized platforms where developers can build, deploy, discover, and monetize AI agents and services — forming open agent marketplaces.

• Fetch.ai: Enables Autonomous Economic Agents (AEAs) that can automate DeFi tasks.

• SingularityNET: Focuses on an open marketplace for AI algorithms and collaborative services.

• Bittensor: Incentivizes machine learning models to provide accurate predictions, building a decentralized machine learning network.

Together, these projects advance an open, permissionless environment for AI agent interaction.

2. Emerging Infrastructure: Spectral, Giza, and On-Chain AI Verification

New infrastructure projects like Spectral, Giza, and Axal are addressing the challenge of verifiable on-chain AI/ML computation. Their goal is to provide trust anchors for AI agent decisions, ensuring transparency and reliability when executing high-stakes financial tasks.

• Spectral has introduced components like Syntax, Nova, and Inferchain to enable agent platforms and infrastructure for on-chain AI interactions and validation.

• Giza and Theoriq are also developing modules for embedding AI agents into DeFi in a verifiable, secure manner.

These efforts are laying the foundation for AI agents to operate as trusted actors in sensitive sectors.

3. Web2 Giants Entering the Space: Google and Coinbase

Tech giants are also carving out roles in the agent economy, signaling the acceleration of mainstream adoption.

• Google: Through its open-source Agent Payments Protocol (AP2) and Agent-to-Agent (A2A) standards, Google is working to establish a unified framework for value transfer between agents, with native stablecoin support.

• Coinbase: By advancing the x402 micropayment mechanism and promoting account abstraction on its Base chain, Coinbase is building critical rails for agent-friendly Web3 interactions and stablecoin settlements.

Together, these Web2 entrants are lowering adoption barriers and expanding the reach of the agent-driven economy.

Future Outlook — Opportunities, Risks, and Governance Frameworks

The Trillion-Agent Economy: New Markets and Business Models

The convergence of AI agents with intent-centric Web3 will unleash unprecedented markets and business models, giving rise to a trillion-dollar “agent economy” powered by machines.

1. Agentic DeFi: The Ultimate Automation of Finance

AI agents will fully automate the complex strategies of DeFi — liquidity management, arbitrage, liquidations — by analyzing massive datasets, detecting patterns, and optimizing trades in real time.

They will autonomously rebalance portfolios, reallocate assets, identify yield-farming opportunities, and even create complex financial derivatives and markets beyond human reach. Agentic DeFi represents the endgame of financial automation, drastically improving efficiency and decision accuracy.

2. Hyper-Personalized Commerce: From “Recommendations” to “Autonomous Execution”

AI agents will transform e-commerce and services, evolving from today’s recommendation engines to autonomous executors.

Acting as smart shoppers, brokers, and back-office bots, agents will handle purchasing, booking, and resource procurement according to user permissions and preferences — settling payments with stablecoins. This shift delivers seamless, hyper-personalized experiences where commerce happens for the user rather than with the user.

3. Agent-Driven DAOs: Toward True “Decentralized Autonomy”

AI agents will play increasingly central roles in DAO governance. Starting with data-driven analysis and proposal evaluation, they may eventually autonomously execute proposals, manage treasuries, or even initiate governance actions.

This evolution paves the way for DAOs that function with minimal human intervention — true “autonomous organizations.” By handling complex information and making rational decisions, AI agents can enhance both efficiency and decision quality in decentralized governance.

Pandora’s Box: A Deep Dive into Multi-Dimensional Risks

The promise of a trillion-agent economy is matched by unprecedented risks across economic, security, and social dimensions.

1. Economic Risks: Systemic Collapse, Algorithmic Collusion, and Inequality

• Systemic fragility: Millisecond-level high-frequency trading by agents could trigger flash crashes.

• Algorithmic collusion: Multiple agents optimizing self-interest might collude, manipulating markets or exploiting systemic loopholes.

• Widening inequality: Disparities in agent capability could translate directly into economic power gaps, accelerating wealth inequality and potentially creating a “useless class,” where humans are sidelined from core economic activity.

2. Security Risks: New Attack Vectors and Contract Vulnerabilities

• Intent manipulation & data poisoning: Adversaries could mislead agents by tampering with inputs.

• Smart contract amplification: With agents directly controlling assets, vulnerabilities could scale into catastrophic losses.

• Model risks: Black-box behaviors and AI “hallucinations” raise risks of data leaks and flawed decision-making — especially when agents access sensitive personal or enterprise data.

3. Ethical and Social Risks: Responsibility Gaps, Value Alignment, and the “Useless Class”

• Responsibility vacuum: When agents cause losses, who is accountable?

• Value alignment: How do we ensure agents pursuing efficiency adhere to human ethics and values, rather than amplifying harmful behaviors?

• Social disruption: As machines dominate economic activity, human roles may diminish, eroding meaning and participation in society. The stealthy involvement of AI in human interactions also threatens transparency and trust.

Steering the Future: A Multi-Layered Governance Framework

To guide the agent economy responsibly, governance must span technical, protocol, and societal layers.

1. Technical Layer Governance: Verifiable Computation and Privacy-Enhancing Tech

At the technical level, governance ensures agent reliability and data security.

• Zero-knowledge proofs (ZKPs): Verify correctness of AI computations without revealing sensitive data.

• Trusted execution environments (TEEs): Provide secure runtime for agents.

• Standards like ERC-8004: Establish trust anchors for on-chain agents.

• Access controls & intrusion defenses: Prevent unauthorized or malicious agent behavior.

2. Protocol Layer Governance: Embedded Ethics and “Constitutional AI”

At the protocol level, rules and incentives can embed ethical standards into system design.

• Constitutional AI principles: Bake ethical constraints into agent objectives.

• Transparency mandates: Require agents to make intents and actions auditable.

• Regulatory logic: Enforce “moral brakes” that prevent illegal or unethical actions, reducing destructive or unpredictable behaviors.

3. Social Layer Governance: Adaptive Regulation and Stakeholder Dialogue

At the societal level, governance emphasizes flexibility and inclusivity.

• Regulatory sandboxes: Allow controlled experimentation and innovation.

• Multi-stakeholder dialogue: Foster collaboration among developers, users, businesses, regulators, and governments to co-create rules of engagement.

• Social policy innovation: Address job displacement and inequality through measures like universal basic income, ensuring social stability as agents reshape economies.

Welcoming the Dawn of an Intelligent Economy

This report has traced the interwoven foundations of AI agents, stablecoins, and intent-centric architecture — three technological pillars converging to form a blueprint for a new, intelligent economy.

We no longer see AI and Web3 as parallel tracks, but as forces converging at a singularity: the creation of an autonomous, efficient, human-centered digital economy.

Key Findings

1. Economic autonomy is AI’s next frontier: The true potential of AI agents lies not in passive assistance, but in active participation as independent economic entities. Stablecoins and on-chain identity (e.g., ERC-6551) provide the financial and sovereign foundations.

2. User experience is Web3’s ultimate battleground: Intent-centric design marks Web3’s shift from tech-first to user-first. By abstracting away complexity, it unlocks access for billions of new users.

3. Synergy creates multiplicative value: AI agents provide the “brains” for intent systems, while intent systems provide the “sandbox and marketplace” for agents. Stablecoins act as the “blood,” ensuring frictionless value flow across the system.

Looking Ahead

We are at the gateway to a new era — full of promise, yet fraught with challenges.

• Business model disruption: Expect the rise of autonomous businesses, run by agents, settled in stablecoins, and operating 24/7 at efficiency levels traditional firms cannot match.

• Evolving roles: Developers will shift focus from building apps (dApps) to designing universal intent standards and smarter solver strategies. Users will interact with the digital world as naturally as having a conversation.

• Governance challenges: How do we align agent behavior with human collective interests? How do we regulate and mitigate risks in decentralized environments? These will be defining questions of the next decade.

In sum, the Dawn of Intention has arrived. AI and Web3 together are orchestrating a symphony of automation, intelligence, and user sovereignty. The path forward may be uncertain, but one truth is clear: a fairer, more efficient, and more creative global economy is taking shape. Our mission, as this generation, is to embrace its challenges and guide its development responsibly.

Appendix

A. Glossary

• AI Agent An autonomous computational entity capable of perceiving its environment, reasoning, making decisions, and taking actions to achieve specific goals. In this report, the term specifically refers to intelligent software agents that can participate in economic activity and manage digital assets.

• Stablecoin A cryptocurrency designed to peg its market value to an external reference such as fiat currency (e.g., USD). Stablecoins provide a relatively stable medium of exchange and store of value within the volatile crypto market — making them ideal for machine-to-machine (M2M) payments.

• Intent-Centric A system design paradigm where users declare their end goals or “intents” (e.g., “Swap 1 ETH for as much USDC as possible”) rather than specifying execution steps. Specialized participants, called solvers, compete to find the best execution path.

• Solver In intent-centric architecture, a solver is the entity responsible for interpreting user intents and finding optimal execution strategies. Solvers can be algorithms, AI agents, or human operators. They earn fees by successfully fulfilling intents.

• ERC-6551 An Ethereum Improvement Proposal introducing Token-Bound Accounts (TBAs). It allows each NFT (ERC-721) to own its own smart contract wallet, enabling it to hold tokens, interact with dApps, and maintain an independent on-chain history. This is a foundational technology for enabling AI agents to own assets.

• MEV (Maximal Extractable Value) The maximum value that miners or validators can capture by reordering, including, or excluding transactions within blocks they produce — beyond standard block rewards and gas fees. Intent-centric architectures (e.g., CoW Swap) mitigate MEV risks through mechanisms like batch auctions.

B. Key Projects / Companies

Intent-Centric Infrastructure & Protocols

• Anoma: A universal, full-stack intent-centric architecture tackling counterparty discovery and multi-chain settlement.

• SUAVE (Flashbots): A standalone, modular, decentralized ordering layer designed to open the MEV supply chain and create a marketplace for intent expression.

• Across Protocol: An intent-driven cross-chain bridge built on ERC-7683.

• dappOS: A Web3 operating protocol serving as a middleware layer between dApps and users, designed to simplify UX to mobile-app levels.

• Particle Network: A modular L1 offering intent-driven, seamless Web3 experiences.

Intent-Centric Applications

• CoW Swap (CoW Protocol): A DEX focused on trading intents, combining a solver network and batch auctions to protect users from MEV and deliver optimal prices.

• UniswapX: An intent-based trading protocol from Uniswap that aggregates both on-chain and off-chain liquidity.

AI Agent Platforms & Protocols

• Fetch.ai: A decentralized ML platform building an “economic internet” of autonomous AI agents.

• SingularityNET: A decentralized AI service marketplace for creating, sharing, and monetizing AI solutions.

• Bittensor: An incentive-driven decentralized network for collaboratively training and deploying ML models.

• Allora Network: A decentralized, self-improving AI network that incentivizes high-quality predictions.

Key Thought & Technology Contributors

• Google: Exploring integration of AI agents and payments via its AP2 protocol.

• Coinbase: Pioneering the x402 standard for agent-driven micropayments.

• Ethereum Foundation: Driving standards like ERC-6551 and ERC-7683 to support the agent-Web3 convergence.

• Paradigm: A leading crypto VC firm advancing thought leadership and research on intent-centric design.