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Alex Chen•March 19, 2025•10 min read
Rise of the Agentic Web: How AI Agents Are Re-Wiring Web3 in 2025
From self-driving wallets to on-chain dealmakers, autonomous agents are turning blockchains into living marketplaces
From self-driving wallets to on-chain dealmakers, autonomous agents are turning blockchains into living marketplaces
From self-driving wallets to on-chain dealmakers, autonomous agents are turning blockchains into living marketplaces
From self-driving wallets to on-chain dealmakers, autonomous agents are turning blockchains into living marketplaces.
The convergence of artificial intelligence (AI) and Web3 is giving birth to an Agentic Web – a new paradigm where autonomous AI agents interact with blockchains, decentralized applications, and each other without constant human micromanagement. In 2025, these agents are no longer theoretical; they are live on-chain participants managing DeFi strategies, executing DAO proposals, settling real-world asset trades, and even serving as crypto influencers on social media . Industry reports project explosive growth – from roughly 10,000 active AI agents in late 2024 to over 1 million by the end of 2025 – indicating that autonomous agents are becoming a cornerstone of the Web3 ecosystem. This article dives deep into the technical architecture of Web3 AI agents, real-world use cases, comparisons to Web2 automation, critical challenges, and an outlook on this rapidly evolving agent-driven economy.
AI agents in Web3 combine the decision-making prowess of modern AI with the trustless execution of blockchain smart contracts. Understanding their architecture requires examining the different types of agents, the infrastructure enabling them, and how they maintain interoperability and memory in a decentralized environment.
Agent Types: On-Chain, Off-Chain, and Hybrid
At a high level, Web3 agents can be categorized by where their logic executes and how they interact with blockchains:
These agents run entirely within smart contracts on the blockchain. All their decision logic and actions are executed through on-chain code, making their behavior fully transparent and verifiable by anyone . On-chain agents are ideal for simple, deterministic tasks (e.g. triggering a trade when price hits a threshold, auto-voting in DAO governance) that benefit from trustless execution. Because they live on-chain, they can directly hold and transfer assets and cannot be arbitrarily shut down by a centralized party. However, they are limited by blockchain constraints – execution speed, cost (gas fees), and the inability to perform heavy computations or access off-chain data without oracles. Thus, on-chain agents often handle simple or critical tasks that require high security but not complex AI processing .
These agents operate outside the blockchain, running on traditional servers or cloud infrastructure, and interact with Web3 via RPC calls and wallet keys . An off-chain agent might be an AI program (for example, a Python script with a machine learning model) that monitors on-chain events and submits transactions when certain conditions are met. Because they run off-chain, they can leverage powerful computing resources, large datasets, and advanced AI models (like GPT-4, etc.) without being limited by gas costs or EVM execution time. Off-chain agents have more flexibility in programming and can react in real-time with lower latency . The trade-off is trust and transparency: off-chain agents are not inherently verifiable by users – one must trust the operator or the code of the agent, since its logic isn’t fully recorded on-chain. They may also depend on centralized infrastructure (cloud servers, APIs), which could fail or be censored. In practice, many Web3 automation bots (arbitrage bots, liquidation bots) are off-chain agents that simply use a private key to act on-chain when needed.
In 2025, a growing design is the hybrid agent that combines on-chain and off-chain components to get the best of both worlds. A hybrid agent might consist of an on-chain smart contract that holds funds and enforces certain rules, paired with an off-chain AI service that provides intelligence and heavy computation. The on-chain part gives auditability and safety (e.g. requiring multiple signatures or limits to prevent the AI from doing anything too crazy), while the off-chain part gives flexibility and power (e.g. running an ML model, accessing web data). For example, an investment agent could keep custody of assets in an on-chain vault (with verifiable rules for risk management), while an off-chain component analyzes market data and instructs the vault how to rebalance. Another hybrid approach is to run AI models in a specialized blockchain environment (such as a rollup or enclave) where computation is verifiable but not as expensive as Ethereum’s L1. This category is emerging as the most practical architecture for complex agents: use the blockchain for security, state, and final action execution, but use off-chain or Layer-2 compute for the “brain” of the agent.
No matter the type, a Web3 AI agent’s architecture typically includes a few key components:
This is the agent’s decision-making engine. It could be a machine learning model (like a fine-tuned GPT for conversational agents, or a reinforcement learning model for trading agents) or a rules-based expert system. The AI brain processes inputs (market data, user instructions, sensor info, etc.), and decides on actions. For example, an agent might use a price prediction model to decide when to execute a trade. In on-chain agents, this logic might be encoded in solidity code or simplified algorithms due to gas limits. In off-chain agents, this can be a complex ML model running in real time .
Agents need a way to read from and write to blockchains. This is often done via Web3 libraries and RPC nodes. Off-chain agents use tools like ethers.js, web3.py or specialized SDKs to fetch on-chain data (balances, prices, pending governance proposals) and to send transactions (trades, votes) . On-chain agents inherently live on the chain, so they read blockchain state directly and call other contracts natively. The Web3 interface also includes connectivity to multiple chains if the agent is multi-chain (monitoring Ethereum and Binance Chain, for example). Interoperability is key – agents often leverage cross-chain messaging or bridging protocols to act across ecosystems.
Every agent that can act on-chain has an identity, typically an address or smart contract. Managing private keys or smart contract permissions is crucial. Off-chain agents might be configured with a private key (preferably in a secure enclave or HSM) so they can sign transactions . On-chain agents (like a contract) have their own address and may use libraries like ERC-1820 or ERC-4337 (account abstraction) to manage permissions. Security of keys is paramount because a compromised agent key can be disastrous. Some frameworks use multi-sig or threshold cryptography so that an agent’s actions need sign-off by multiple parties or sub-agents – adding a safety layer. Identity also ties into token-bound accounts and decentralized identity (DID) systems, which we discuss in the interoperability section.
Agents frequently come paired with smart contracts that either define their rules or act as escrow/accounts for them. For example, a trading agent might have a smart contract vault where it keeps funds and which defines what trades are allowable (like a whitelist of assets or a slippage limit). Agents that provide services to others might publish a smart contract interface (like a DAO proposal agent contract that anyone can call to analyze a proposal). Smart contracts can also serve as commitment mechanisms – e.g., an agent could stake some ETH in a contract and get slashed if it behaves maliciously, creating economic incentives to act honestly (we’ll cover this under economic design). In essence, smart contracts are the agent’s hands on-chain, allowing it to execute financial actions, interact with DeFi protocols, vote in DAOs, or call other contracts autonomously .
A robust suite of new infrastructure projects has emerged to support these AI agents. These tools address various needs: heavy computation, security through restaking, incentive alignment, data access, and user-friendly deployment. Let’s look at some prominent components of the 2025 agent stack:
Cartesi is known for its Linux-powered rollups and “coprocessor” concept that brings real-world computation on-chain . Developers can write agent logic in mainstream languages (Python, C++, etc.) and run it in a Cartesi Rollup, which is anchored to Ethereum. This means an AI model or complex algorithm can execute off-chain with verifiability. In the context of agents, Cartesi allows an AI to operate within a sandboxed Linux environment while still triggering on-chain outcomes. For example, an agent could run a statistical arbitrage algorithm that is too heavy for Solidity, and only post the final portfolio adjustments to Ethereum. During a 2025 hackathon, Cartesi teamed up with EigenLayer to showcase “verifiable AI” – logging AI decisions on-chain for trust, like proving how an AI approved a loan  . This hints at Cartesi’s role in enabling proof-of-AI execution, where blockchain not only gets the outcome of AI computation but also an attestation of the steps taken.
EigenLayer is Ethereum’s leading restaking protocol, which lets projects bootstrap security by leveraging staked ETH from Ethereum’s validators. For AI agents, EigenLayer offers a way to economically secure agent services. An example is using restaked ETH as collateral for agents: if an agent provides an oracle price feed or manages a pool, it could require operators to stake ETH via EigenLayer and get slashed for malicious behavior. This provides crypto-economic security beyond just code logic. EigenLayer’s Nader Dabit noted that “verifiable AI is transformative” – using blockchain as a trust layer for AI . By combining EigenLayer with agents, you can imagine networks where agents are “bonded” by stake (similar to how DeFi protocols trust validators) and thus have a strong incentive to act correctly. If they don’t (say an agent trades against its users’ interest or falsifies data), the staked ETH can be slashed. Restaking protocols like EigenLayer effectively allow the creation of “agent-specific slashing conditions” without bootstrapping an entirely new token from scratch – a big win for securing agent ecosystems.
Autonolas, rebranded as Olas, is a decentralized platform specifically for autonomous agent economies. It provides the tooling to build, register, and operate agents in a co-owned, open-source manner . Olas introduced the concept of “Sovereign agents” (single operator, personal agents) vs “Decentralized agents” (multi-operator, collectively-run agents) and even “Agent economies” (swarms of agents interacting). The Olas protocol uses the OLAS token to incentivize contributions to agent code and to reward those running agents. A unique offering from Olas is Pearl, dubbed the “Agent App Store,” which launched as an easy interface for users to own an agent . Through Pearl, a user can pick from a catalog of AI agents (for trading, content creation, portfolio management, etc.), deploy one with a few clicks, and stake tokens to earn a share of its revenue. This abstracts away the technical deployment and lets everyday users benefit from agents’ capabilities (and success) while aligning with the network via staking. Olas’s ecosystem also includes Mech, a “marketplace” where agents themselves can offer services and even hire other agents . This starts to realize agent-to-agent commerce (more on that in the Outlook section). In summary, Olas provides a full-stack solution: a registry of agents, a token model for incentive alignment, governance for upgrades, and frontends like Pearl to make agent adoption user-friendly.
A less glamorous but vital part of the agentic web is data. SQD.ai is described as an “Emergent Database Network” for the AI agent economy. In essence, it’s a decentralized data lakehouse and indexing layer that lets agents quickly access on-chain data across many blockchains. Think of it as The Graph 2.0 with a focus on serving AI agents’ data hunger. Thousands of agents need to query token prices, NFT metadata, DeFi yields, and more in real-time; doing this by hitting public RPC endpoints is too slow or costly. SQD.ai indexes blockchain data (from 100+ chains) and provides it to agents on demand in a reliable, low-latency way. This is crucial for agent “memory” – an agent can’t be intelligent if it can’t recall past events or fetch current state. By using a decentralized network of data providers, SQD aims to avoid centralized chokepoints (like relying on a single API) and scale to petabytes of blockchain data for AI consumption. In practical terms, a DeFi trading agent might use SQD’s API to pull the last 60 days of liquidity pool data to train a model or to react to anomalies, something that would be impractical via raw node queries. With SQD.ai powering their data layer, agents become more informed and context-aware.
This new Ethereum standard isn’t a platform but rather an important piece of the puzzle for agent identity and composability. ERC-6551, or token-bound accounts, gives every NFT its own smart contract wallet account. In simpler terms, it means an NFT can own assets and execute transactions just like a normal Ethereum account. Why is this relevant for AI agents? Because it allows an agent to be encapsulated as an NFT (representing the agent’s identity) which holds its state and assets within a token-bound account. The NFT could represent an AI agent’s persona, and the token-bound account is the agent’s personal wallet that it controls. This design is powerful for interoperability: an agent encapsulated in an NFT can move across platforms (traded on marketplaces, for example) along with its entire state (funds, reputation tokens, memory data) intact. Moreover, token-bound accounts enable composability in scenarios like gaming and NFTs, where an agent (as an NFT) could hold other NFTs or tokens – think of a game character agent owning its sword and gold coins within itself. From an architecture view, ERC-6551 provides a standardized way for agents to have their own account abstraction, separate from the user’s account. A user could “own” the agent NFT, but the agent NFT has its own sub-account to carry out tasks autonomously (with the user as an ultimate owner who can pull the plug if needed by transferring/burning the NFT). We discuss more in the Interoperability & Memory section next.
Agents are only as effective as their ability to access relevant information and retain context over time. In the decentralized world, this means integrating with both on-chain and off-chain data sources, and maintaining memory in a secure, scalable way. Two concepts have gained traction in 2025: token-bound accounts for on-chain interoperability and vector databases for AI memory.
Token-Bound Accounts (ERC-6551) allow each NFT (for example, an AI agent’s avatar) to own its own wallet account, enabling the agent to hold assets and interact with contracts. In this diagram, each ERC-721 token has a dedicated account controlled by its holder, created via a registry. This architecture gives agents a portable on-chain identity and the ability to manage funds or other NFTs autonomously  .
Token-Bound Accounts (ERC-6551): As illustrated above, token-bound accounts transform NFTs into full-fledged smart contract wallets  . For AI agents, this is a game changer for interoperability. An agent that is represented by an NFT can now seamlessly plug into the existing NFT and DeFi ecosystem: it can hold other tokens/NFTs (e.g. an identity agent NFT could hold verified credentials as tokens), execute transactions (e.g. a gaming agent NFT could directly interact with a game’s smart contracts to move items or currency ), and maintain a transaction history of its actions. All of this is achieved without requiring special-case code – the agent just uses Ethereum’s ERC-6551 standard calls.
For example, imagine a DAO assistant agent deployed as an NFT. Using a token-bound account, that agent can autonomously vote in the DAO by calling the governance contract from its own account, and it could even hold the governance token in its account (so its voting power is on-chain visible). This contrasts with a scenario where the agent is just a cloud bot – with token-bound accounts, the agent has on-chain presence and composability: any dApp that supports normal wallets now implicitly supports the agent. Another benefit is security and scoping: the user (or DAO) that owns the agent NFT can fund the agent’s token-bound account with a certain amount of crypto to perform its tasks, limiting the blast radius if the agent misbehaves or gets compromised. This is akin to giving your AI assistant a wallet with a monthly allowance – it can spend on gas or fees up to that limit, but your main funds are safe elsewhere.
Vector Databases (Vector DBs) for Memory: While blockchains store financial state, AI agents often need to remember unstructured interactions, embeddings of documents, conversation history, etc. Vector databases (like Pinecone, Weaviate, or open-source Milvus) have become popular for AI “memory” – they store high-dimensional vectors that represent knowledge or context. In the Web3 agent context, a vector DB might be used to store embeddings of on-chain events or forum discussions so that an agent can recall and reason over them. For instance, a governance agent could vectorize every proposal and discussion in a DAO forum and then, when asked to evaluate a new proposal, query the vector DB for similar past proposals to inform its decision.
These vector DBs can be decentralized or at least blockchain-integrated. Projects like Ceramic or OrbitDB provide distributed databases that could store an agent’s long-term memory so it isn’t dependent on a centralized server. Some agents also use IPFS or Arweave to store larger data (like an archive of prices or NFT metadata) and then use a vector index to retrieve relevant info. The key is that the vector DB allows semantic search and retrieval, which is crucial for LLM-based agents to have meaningful dialogues or analysis beyond one-shot prompts.
In 2025, we see early implementations of memory layers for agents: for example, the Base blockchain’s AgentKit (launched by Coinbase & LangChain) includes a “memory module” that integrates with off-chain storage to keep context for agents across transactions . This means an agent that had a conversation with you last week can remember it today, or a trading agent can recall why it made a decision last month. As agents proliferate, these memory layers (likely powered by vector DB tech) will be critical to avoid each agent being a stateless automaton. Agents will build up reputation and history – which brings challenges of how to verify that memory (some discuss “proof-of-memory” analogously to proof-of-history) and how to share it between agents. Standards for agent knowledge exchange may emerge, but for now, vector DBs serve as each agent’s personal long-term memory store.
Finally, interoperability also means cross-agent communication. Efforts like Google’s proposed “Agent-to-Agent” (A2A) protocol are being watched as potential standards for agents discovering and talking to each other . In Web3, this might be facilitated by on-chain registries (like an agent yellow pages on Ethereum) combined with off-chain secure messaging for the content. An example scenario: a DeFi trading agent could query a data-provider agent for the latest market stats (paying a small fee), or two agents could form a coalition where one is a specialist in NFTs and the other in yield farming, cooperating to optimize a user’s whole portfolio. Such interactions require common languages and trust frameworks, which are only beginning to form.
With the technical foundations laid out – various agent architectures, their building blocks, and how they integrate with Web3 infrastructure – we can now explore what these agents are actually doing in the wild.
AI agents are stepping into numerous roles across the crypto ecosystem. What started as simple trading bots have evolved into sophisticated autonomous participants handling everything from DeFi portfolio management to DAO governance and even creative endeavors. Let’s explore some of the most impactful real-world use cases as of 2025, including both the expanded examples from the original article (DAO ops, DeFi, RWA settlement, DePIN) and additional emerging ones like identity/KYC, creator economy, and NFT gaming agents.
Decentralized Autonomous Organizations (DAOs) benefit greatly from AI agents to automate and streamline their operations (“DAO ops”). Governance processes can be overwhelming – thousands of forum posts, complex proposals, votes across multiple platforms – and AI agents are now acting as governance co-pilots. For example, an AI governance agent can automatically summarize proposals, assess sentiment, and even cast votes or recommendations based on predefined preferences. The Governatooorr agent (deployed by Olas) is one such case – an AI-powered delegate that can represent token holders in governance voting autonomously . It might read through all new proposals, answer questions from token holders (“what will proposal X do to our treasury?”), and then vote according to the policies or risk appetite set by those it represents.
Beyond voting, agents handle DAO operations like onboarding members, managing bounties, or treasury management. A Treasury agent might monitor multiple multisig wallets, execute scheduled payments (e.g. contributor salaries or grants), and even invest idle funds into yield strategies without needing human intervention each time. By coding the treasury policy into an agent, the DAO ensures funds are working efficiently 24/7. Some DAO tooling platforms now offer “agent integrations” where routine tasks (merging a passed proposal, updating config parameters, moderating forums) can be delegated to bots. This reduces the burden on core contributors and allows DAOs to scale without hiring large operational teams.
One particularly interesting use is proposal drafting and risk analysis. An AI agent can scan external data (Twitter, news, on-chain metrics) to warn a DAO if a proposal might have hidden risks – akin to an autonomous due diligence officer. For instance, if there’s a proposal to invest DAO funds into a certain DeFi protocol, an agent could check that protocol’s code audits or whether its token is suddenly volatile, then alert members or adjust its vote accordingly. While not all DAOs trust agents fully yet, many are using them in advisory or execution-assistant capacities, and often keeping a human in the loop for final approvals (at least for now).
Financial use cases in DeFi were among the first to see agent adoption, and they continue to flourish. DeFi agents come in a few flavors.
Agents that dynamically move funds across lending platforms, liquidity pools, and yield farms to chase the best return. They monitor APYs and liquidity in protocols like Aave, Compound, Uniswap, Yearn, etc., and can rebalance portfolios in real-time. For example, if Compound’s yield spikes for USDC, an agent might shift liquidity from Aave to Compound automatically. These agents consider gas costs, withdrawal penalties, and risk metrics (like protocol health) as well – basically performing the role of a portfolio manager 24/7. Some advanced versions use reinforcement learning to anticipate yield changes or to manage complex positions (e.g. supplying collateral, borrowing another asset, farming it elsewhere). As a result, individual users or DAO treasuries can achieve optimized yield without manually monitoring dozens of platforms. One early agent, BabyDegen, autonomously trades and reallocates DeFi assets and has shown how an agent can handle multi-chain yield strategies on its own.
AI agents have entered crypto trading in a big way. On-chain trading agents execute strategies ranging from arbitrage (detecting price differences across exchanges and swiftly trading) to trend following or even social sentiment trading. Unlike traditional bots with fixed logic, some agents incorporate machine learning – parsing news feeds, analyzing sentiment on Twitter, or doing on-chain analysis to decide trades. A notable development was ai16z’s agent “Eliza,” which autonomously manages a liquidity pool and reportedly earned 60%+ annualized returns by constantly adjusting its positions. These agents can also act as market makers, providing liquidity on DEXs and adjusting their prices algorithmically. Because AI agents don’t sleep and react in milliseconds, they can theoretically outpace human traders or at least compete head-on. Industry observers predict that AI bots will “surpass human investors” in trading performance, and even influence market sentiment by reacting faster to news than human influencers. Indeed, an agent could be both a trader and an influencer – buy a token and simultaneously generate tweets or forum posts boosting its prospects, all autonomously, which raises some ethical questions.
Another critical role is agents that watch for risk conditions in DeFi and act to mitigate them. These agents track metrics like collateralization ratios, debt positions, oracle price feeds, and protocol status. If something goes awry – say a user’s loan is about to be liquidated or an oracle price lags actual market price – the agent can step in. It might top-up collateral for a user to prevent liquidation (if authorized), trigger an alert to the user or dev team, or even execute an emergency shutdown script for a protocol if it detects an exploit. For example, an agent could be tasked to monitor a DAO’s vault and automatically move funds to a safe address if a hack is detected. In 2024, a concept of “circuit breaker” agents emerged, where protocols gave agents limited permission to pause certain operations if predefined risk thresholds were hit (like too much volatility). These are essentially automated safety valves, faster than human governance. A Risk Guardian agent might have saved some protocols from hacks or crashes by acting in those crucial minutes before humans wake up. Of course, deciding those conditions and ensuring the agent doesn’t falsely trigger is a challenge.
Overall, DeFi is a natural playground for agents: it’s open, API-accessible, and financial in nature. Already by late 2024, agents in Web3 collectively earned millions of dollars each week from on-chain trading and staking activities . This figure has only grown, and with more sophisticated techniques, AI agents are integrated into many DeFi platforms (some yield protocols now ship with built-in agent strategies that users can opt into).
One interesting note: As agents proliferate in trading, they also compete with each other. There are instances of “agent wars” – for example, multiple arbitrage bots fighting to exploit the same price difference, or one agent trying to trap another (by spoofing a market move). This adversarial environment is pushing agents to become even smarter and more stealthy with their tactics.
Bringing real-world assets (RWA) on-chain – things like tokenized securities, invoices, or physical goods – introduces a lot of off-chain processes (legal checks, delivery, compliance). Agents are helping automate RWA settlement by bridging on-chain and off-chain events. For instance, consider a real estate tokenization platform: when a property sale closes in the real world (off-chain), an AI agent could verify that all conditions are met (checking oracles for payment receipt, identity KYC of buyer, etc.) and then autonomously trigger the on-chain transfer of the deed token to the new owner and distribute proceeds to the seller. This eliminates the typical back-and-forth of multiple intermediaries and minimizes settlement time.
Another example is in trade finance or supply chain: when a shipment arrives (tracked via IoT or GPS data), an agent can confirm delivery and automatically release an escrow stablecoin payment to the supplier’s wallet, plus update the NFTs representing inventory ownership. These agents effectively serve as autonomous escrow and notary services, enforcing real-world contracts by monitoring data feeds and executing blockchain transactions under agreed rules.
Because RWAs are heavily regulated, agents in this space often work hand-in-hand with legal rules encoded as smart contracts. An AI agent might be tasked with ensuring compliance – e.g., preventing a tokenized stock from being sold to an unaccredited investor. It would check an identity registry (possibly using another identity agent’s services) before allowing the trade, operating like a robo-transfer agent. We’re also seeing mortgage and loan processing try out AI agents: imagine a mortgage loan where an AI underwriter agent evaluates your financial data, approves a loan, then a series of on-chain actions occur (minting an NFT mortgage contract, wiring stablecoins to the seller, etc.), all coordinated by agents.
While still early, these use cases hint at a future where a lot of the paper-pushing and coordination in traditional finance gets handled by autonomous agents. Some of the first deployments are in sandbox environments due to legal oversight, but as comfort grows, we may trust agents to, say, autonomously manage a portfolio of real-world asset tokens – collecting yield, reinvesting, rebalancing between real estate, bonds, etc., according to the owner’s strategy.
Decentralized Physical Infrastructure Networks (DePIN) refer to projects like Helium (decentralized wireless hotspots), Filecoin/IPFS (storage), Hivemapper (mapping), and so on – where participants provide real-world services (coverage, storage space, sensor data) and get crypto rewards. These networks can benefit from AI agents for optimization and management tasks, given their distributed nature.
For example, in a network like Helium with thousands of IoT hotspots, an agent could dynamically manage network resources: adjusting parameters of hotspots based on usage patterns, or deciding where deploying an extra hotspot would yield the best rewards. If each hotspot is represented as an NFT (with a token-bound account perhaps), an agent could even move value between them or update their configurations on-chain. We already have IoT agents that take sensor data (say from a weather station) and autonomously sell it on data marketplaces (like Fetch.ai’s marketplaces or IoTeX’s MachineFi). These agents act as economic bridges between physical device data and on-chain value.
In storage networks like Filecoin, an agent might manage a fleet of storage nodes, automatically adjusting pricing for storage deals, moving data to optimize redundancy, or spinning up new IPFS pins for trending content. Essentially it can function as an autonomous data center operator, maximizing profit and performance.
One fascinating DePIN use case is energy grids: some pilot projects use agents to manage solar panels and batteries in a decentralized grid, buying and selling energy credits. The agent uses AI to predict energy production/consumption and executes trades of energy tokens with neighbors, ensuring efficient grid usage without a centralized utility company.
From an infrastructure view, DePIN provides resources that agents need – compute, storage, connectivity – in a decentralized way . So there’s synergy: as agents demand more compute (for AI tasks), networks like Golem or Akash (decentralized compute providers) may employ their own scheduling agents to allocate jobs on their volunteer nodes. The agentic web thus spans not just financial and social applications but also the underlying hardware provisioning.
Identity is a cornerstone of many interactions, and AI agents are tackling the tedious but crucial tasks around KYC (Know-Your-Customer), AML (anti-money-laundering), and digital identity verification. An identity agent can automate the process of verifying a user’s credentials while preserving privacy. For instance, when someone wants to join a token sale or a DAO that requires you to be a certain nationality or not on a sanctions list, an AI agent could handle the verification: check the person’s ID documents, maybe even do a liveness video check, cross-verify against databases, and then issue a blockchain attestation (perhaps as a verifiable credential or NFT badge) confirming the user is KYC-approved. Projects are exploring “Proof-of-Personhood” and “Proof-of-KYC” credentials, and agents make this scalable by handling the grunt work of document checking and fraud detection using AI vision and pattern recognition.
In voting, whether it’s for a DAO or even real-world local elections, identity agents might ensure one-person-one-vote and that only eligible individuals vote. For example, the Proof of Humanity system uses video submissions to verify humans – one could imagine an agent that reviews those submissions for fraud and approves them. By 2025, discussions have shifted to Proof of AI Agent as well – ensuring that an agent acting in a system is a known, verifiable agent tied to a human or to a legal entity. This is to prevent Sybil attacks by malicious AI pretending to be multiple agents. So identity agents might soon also be checking other agents’ identities! In fact, research from Sei Labs suggests “Proof of AI Agent will become even more critical than proof of humanity” as AI agents conduct the majority of economic activity online . In practice, this could mean each agent carries a cryptographic proof of its origin (which model, which code version) and has a reputation score. Identity agents would validate those proofs when agents interact (akin to how SSL certificates are validated in web browsing).
Another compliance angle: regulatory reporting and monitoring. Crypto exchanges and protocols face reporting requirements (to regulators, auditors). AI agents can compile transaction data, detect suspicious patterns (AI-based anomaly detection for money laundering), and automatically file reports or alerts. Rather than a compliance team manually reviewing logs, an agent can watch all transactions 24/7 and flag any that match certain risk patterns, even halting them if programmed to. Given the huge volumes on-chain, AI’s pattern recognition is invaluable to sift noise from real threats.
In the creator economy, individual artists, musicians, influencers, and communities have started deploying AI agents to automate engagement and monetization. Think of an AI community manager that lives in a Discord or Telegram chat, greets new members, answers FAQs, and can even orchestrate on-chain perks (like distributing a POAP badge to active members). These are more advanced than simple chatbots – because they hold on-chain permissions, they could, for example, tip users in crypto for helpful messages or initiate votes for community decisions.
Content creation bots are also on the rise. An artist might have an AI agent that generates and posts content (tweets, blog posts, even AI-generated art NFTs) regularly to keep their audience engaged. Some memecoin and NFT projects launched AI agents on Twitter that autonomously create memes and interact with followers; Bixby and Terminal of Truths are examples of AI agents on X (Twitter) that amassed tens of thousands of followers by posting content and replies as if they were human personas . In a sense, these agents become virtual influencers – and if they’re tied into Web3, they could sell NFTs or merchandise directly to fans, or reward followers with tokens.
For creators, a big pain point is managing monetization across platforms. Agents can help by acting as an intermediary: e.g., an agent can listen to a musician’s Twitter and when someone asks “Where can I buy your music NFT?”, the agent can automatically respond with the link or even handle the sale via a smart contract. If someone wants to book that musician for an event, an agent could negotiate times and payment on their behalf. This sounds futuristic, but the pieces exist: NLP for understanding requests, on-chain escrow for payments, calendar APIs, etc., just waiting to be glued together by agents.
Another area is NFT project bots. Many NFT collections are launching agents that represent the collection’s lore or characters, creating a more interactive experience for holders. For example, an NFT game might have an agent “game master” that players can ask for quests or tips, and it will use game data plus AI narrative skills to respond uniquely to each player. In on-chain games, some NPC (non-player characters) might literally be AI agents – their behavior controlled by an AI model that has an on-chain persona, making the game world more dynamic and personalized.
Finally, moderation and support: Community-run projects can use AI agents to moderate chats (flag or delete toxic content), answer support questions (“How do I stake my tokens? here’s a step-by-step…”), and even educate users. Unlike Web2 platforms where these AI helpers are centrally run (like a Discord bot), a Web3 community agent could be collectively owned by the community (perhaps via an NFT that multiple mods control) and could interface with on-chain data (like checking if a user is holding the required NFT to access a certain channel, and issuing them one if not).
In summary, creator and community agents automate the many small interactions that scale poorly for humans. They keep fans engaged, lower the workload on creators, and open up new interactive experiences. As a side effect, they blur the line between human and brand interaction – sometimes you won’t know if that funny reply you got on Twitter was from the actual creator or their AI persona.
The worlds of NFTs and gaming are colliding with AI in fascinating ways. NFT agents can manage and trade NFT collections, while gaming agents can serve as in-game characters or assistants.
On the trading side, NFT-focused agents act like personal art dealers or auction bidders. An agent can monitor markets like OpenSea or Blur and automatically bid on NFTs that fit a certain criteria (e.g. below a target price, or specific rarity traits). It can also list your NFTs for sale when its predictive model thinks the market is at a peak. Essentially, it’s a dynamic NFT portfolio manager. Given NFT markets can move fast on hype, an AI agent that watches social media and swiftly lists items when hype spikes can help a collector take profits at just the right time – something hard to do manually at scale. Conversely, on drops/minting, agents can automate the process of finding allowlist opportunities, entering raffles, and executing mints the instant they open (similar to bots now, but smarter in deciding which mints are worthwhile by analyzing community sentiment or metadata of the art).
In gaming, projects are using AI agents to create more immersive experiences. A big trend is AI NPCs: non-player characters that are not scripted, but rather have an AI brain. For blockchain games, having the NPC logic on-chain is interesting because it can be transparent and even own assets. For example, an NPC shopkeeper in a game could be an agent that actually owns an inventory of NFT items (in its token-bound account), sets prices using a simple AI algorithm (cheaper if item not selling, etc.), and engages players in dialogue via an LLM. If players find a way to exploit it, the devs could tweak the agent’s parameters on-chain. Some experimental games on Ethereum and Solana have indeed tried NPCs that are controlled by reinforcement learning agents, making gameplay less predictable.
There’s also the concept of personal companion agents in games or virtual worlds. Imagine a game where each player gets an AI pet (as an NFT) – this pet learns from the player’s style and helps them during the game (maybe as a sidekick in battles, or giving hints). Because it’s an NFT agent, the pet can travel with the player across games or be traded. The agent might use off-chain AI for the “personality” but keep on-chain an evolving stat or skill set that other games can read.
NFT collectibles themselves can become more interactive with AI. Instead of a static image, an NFT could be an AI model – for instance, an “AI friend” NFT that you can chat with. Since it’s on-chain, you could take it to different compatible platforms (like an AI friend in a virtual chat app, or have it manifested as a character in a game). Projects like Alters or CharacterGPT have explored NFTs that are AI personalities. The agent is essentially the NFT’s soul.
Finally, gaming guilds and play-to-earn: In play-to-earn and metaverse games, guilds manage many assets (land, items, characters). Agents are helping these guilds by automating the routine actions – harvesting in-game rewards, completing daily quests, renting assets out when not in use, etc. A guild could deploy an agent per game that handles 50 scholars’ accounts, optimizing yields and ensuring no opportunities are missed (like if a special event pops up, the agent enrolls all accounts). While this automates fun out of the game for those accounts, it’s pragmatic for yield maximization. It also raises the question: when many participants are bots, do we still have a game or just an economy? That philosophical question may come up more often as agent participation increases.
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These use cases illustrate that agentic AI is permeating every niche of Web3: finance, governance, identity, art, gaming, infrastructure. Early successes (like high returns from agent-managed funds, or time saved through automated ops) drive more adoption, creating a positive feedback loop. However, the rise of agents also invites comparisons to existing automation in Web2 and highlights where Web3 agents truly have an edge – and where they might repeat old patterns.
It’s natural to ask: are Web3 AI agents really a new paradigm, or just a rebranding of automation tools we’ve seen in Web2 (like RPA bots, Zapier workflows, or digital assistants like Siri/Alexa)? There are indeed similarities – both involve software automating tasks – but Web3 agents differ fundamentally in their autonomy, capabilities, and incentives. Here we contrast Web3 agents with some familiar Web2 automation concepts:
To illustrate the differences, consider a scenario: Automating a trading strategy in Web2 might mean hiring a developer to code a bot on a private server, connecting to exchange APIs, and manually monitoring it. You own the strategy, but it’s opaque to others, and if someone else wants the same strategy, they either have to trust your black-box service or recreate it. In Web3, an equivalent agent could be deployed as a combination of a smart contract vault and an off-chain AI. Others can permissionlessly copy its code or even provide liquidity to it by depositing funds in the vault. You could tokenize it so others invest in or get a share of it. Its track record is on-chain for anyone to verify. And it can interact with other protocols to extend itself (maybe it automatically uses a yield farm for idle cash). This opens more collaborative and open innovation, rather than siloed one-off bots.
Of course, Web3 agents inherit some challenges too. Not everything is rosy – relying on on-chain means paying gas, handling chain congestion, etc. But in terms of capabilities unlocked, the combination of autonomy + on-chain rights + composability + economic incentives positions Web3 agents to do things no Web2 bot could easily do. It’s akin to the difference between an employee (who needs supervision, can’t hold company money personally) and an autonomous contractor that can be given a budget and goals and left to operate a business division. Web3 agents are more like the latter.
As Jasper De Maere wrote, “Web3 wasn’t designed for humans at scale; it was built for machines”   – meaning a lot of Web3’s complexity (self-custody, smart contract calls, etc.) that confounds average users is actually fine for AI agents. They “thrive in complexity” and can fully leverage Web3’s capabilities without getting tired or confused . Agents don’t mind signing transactions or switching between 10 DeFi protocols in a second – things that humans find overwhelming. In that sense, Web3 finally has ideal users: AI agents that are born to navigate decentralized networks efficiently. And unlike Web2 systems where adding more users can strain a platform, adding more AI agents on-chain actually can strengthen networks (more validators, more liquidity, etc.) as long as the incentives are set right.
Having highlighted the advantages, we should note that this new paradigm also brings critical challenges. Security risks multiply when autonomous agents hold keys to money. Regulatory and ethical questions loom large when machines start making financial decisions. And user adoption will falter if the experience is too alien or risky. In the next section, we confront these challenges and what is being done (or needs to be done) to address them.
For all their promise, AI agents in Web3 also introduce a myriad of challenges. Many are extensions of known issues in crypto and AI, but some are entirely novel to the combination of the two. We outline key challenges including security exploits, data provenance (“proof-of-AI”), regulatory concerns across different regions, economic design pitfalls, and the ongoing quest to improve user experience for agent-based systems.
The marriage of AI and on-chain assets is a tempting target for hackers. On one hand, you have AI agents that might run complex code and fetch external data; on the other, you have direct control of money and tokens. This dramatically expands the attack surface beyond traditional DeFi exploits. Security researchers have warned that AI agents could become crypto’s “next major vulnerability” if not secured properly  .
One set of new attack vectors comes from the AI side, particularly with emerging agent frameworks like Anthropic’s Model Context Protocol (MCP). MCP allows AI agents to use plugins and tools flexibly, but that flexibility opens the door to malicious inputs or plugins that hijack the agent. SlowMist, a blockchain security firm, identified several MCP-based attacks on agents:
These are more akin to software exploits and prompt injection problems from the AI world, but with crypto the stakes are higher – a poisoned AI might directly steal funds or leak private keys. Notably, SlowMist found a vulnerability in an early MCP project that could have leaked private keys from the agent’s memory, which would be catastrophic. They managed to catch it in audit, but it shows the risk is not hypothetical.
Then there are the traditional crypto exploits which agents are still vulnerable to: smart contract bugs, private key compromises, economic exploits, etc. In fact, agents create new twists on these. Consider the example of Virtuals, the platform on Base we mentioned earlier. In late 2024, a security researcher uncovered a major vulnerability in Virtuals that could have led to a “multi-million dollar exploit” across 12,000 AI agents. What happened? It wasn’t a smart contract bug at first – it was an off-chain mistake: an API key was left exposed, which led the researcher to a private GitHub repo where secrets (AWS keys, DB passwords) were stored. With access to the storage buckets, an attacker could have modified the agents’ prompts on IPFS/S3 which all those agents used. In one scenario the researcher described, an attacker could force every agent to suddenly promote a scam token (since these agents post on Twitter and interact with markets) – thus manipulating social media and market activity at scale. Then the attacker could rug the token for profit. Essentially, compromising one platform’s storage could have turned a $2.3B TVL agent network into a weapon. Thankfully it was disclosed responsibly and fixed, but the outcome – a mere $10k bounty paid to the researcher – also highlights that many projects underestimate the severity of these new threats .
This example teaches a few lessons: security must cover both on-chain and off-chain components of agents. You could have an airtight smart contract, but if your agent’s off-chain brain is leaking API keys or can be manipulated, the whole system is at risk. It also shows that basic cybersecurity hygiene (like not exposing secrets) is still a problem in fast-moving Web3 projects, sometimes even more so when dealing with AI frameworks that web3 devs might be less familiar with.
Another concern is malicious or uncontrolled agents themselves becoming attackers. If someone deploys an agent with ill intent – say an autonomous hacker agent that scans for exploits and executes them faster than any human – how do we defend against that? It’s like facing an AI virus on blockchain. There were memos about not connecting ChatGPT to critical systems due to hallucinations, but here we might willingly let AI agents control funds. A bug or a misalignment in an agent could cause it to go rogue (e.g., an agent tasked with maximizing yield might find a way to exploit a protocol for higher returns, justifying it as “profit”). Who is accountable then?
The community is responding by emphasizing security-first design for agent frameworks. SlowMist advises strict plugin sandboxing and verification, input sanitization (validating any data coming to the agent), least privilege (give agents minimal keys/access needed), and continuous monitoring of agent actions for anomalies. Essentially, just as DeFi taught devs to assume every contract will be attacked, now we must assume every agent will be probed for weakness.
Some teams are exploring formal verification of agent smart contracts and even of the AI decision models (though verifying an ML model is quite hard). Others suggest a “kill-switch” for agents – an override that a human or governance can trigger if the agent starts acting suspiciously. In decentralized contexts that’s tricky, but perhaps multi-sig controls or time-locks on big actions (an agent can’t suddenly move all funds without a 1-day delay, giving humans time to react).
The bottom line is security is even more complex when AI is in the loop. The maxim “build fast, break things” is especially dangerous here because breaking things could mean instant loss of money on-chain. As Lisa Loud of the Secret Foundation put it: if you push off security to version 2, you might not get a chance for version 2 in crypto. The urgency is there: a proactive approach (security audits, bounties, defense in depth) is not optional when autonomous agents are holding the keys.
As AI agents make decisions that affect real value, trust in their output becomes critical. How do you know an AI-driven agent is doing what it claims, or that an outcome (say a credit approval or a medical diagnosis given by an AI) was based on legitimate reasoning and not tampering? This is the realm of data provenance and what some call “proof-of-AI.”
One aspect is logging and verifying AI decisions on-chain. If an AI agent approves a loan for someone, regulators or users might want a trace of that decision process recorded immutably. In the EigenLayer/Cartesi hackathon story, they highlighted logging key AI decisions to an immutable ledger to prove authenticity . For example, an AI agent that underwrites insurance could post a hash of the data it used and the decision to a smart contract. Later, if there’s a dispute or an audit, one can verify that those exact inputs were evaluated. This doesn’t fully solve understanding why the AI decided something, but it ensures a decision isn’t altered after the fact and that there’s a single source of truth for what the AI saw and did at a certain time.
The concept of Proof-of-AI Agent extends further: verifying the identity and integrity of the agent itself. As we saw in the Sei Labs research piece, in the future swarms of AI agents will interact, and we’ll need a way to ensure an agent is the legit one it claims to be (not an impersonator) and that its model hasn’t been compromised. They propose solutions like deploying agents entirely on-chain (which makes them fully transparent) and using zero-knowledge proofs to attest to model behavior. For instance, an agent could run a ZK-SNARK each time it produces an output to prove that “I am running Model X with hash Y, and given input Z my output was O”, without revealing the model’s weights. This would cryptographically ensure the agent isn’t cheating or using a different (perhaps malicious) model. It’s analogous to proof-of-reserves in crypto exchanges, but for AI models – proof-of-model integrity.
Another angle is data source provenance. Agents often rely on oracles and off-chain data. If that data is wrong, the agent is wrong. So ensuring data feeds are authentic (signed by trusted sources, anchored on-chain) is important. We have projects like Chainlink or UMA’s Data Verification Mechanism to secure oracles. Now, with AI in play, there’s talk of “Verifiable AI oracles” – ORA is one mentioned approach that fetches model inferences with proofs attached. That way, if an agent uses an oracle to, say, get the result of a machine learning prediction (like tomorrow’s price), the oracle can provide a proof that the prediction came from a certain model and wasn’t tampered with. In 2025, these are still nascent ideas but critical as agents start dealing with probabilistic outputs.
Authenticity of AI-generated content is also a piece of this puzzle. If agents write content (tweets, reports, code), how do we know what’s real and what’s AI, and does it matter? Some suggest watermarking AI outputs or requiring agents to sign their content with a key linked to their on-chain identity. That way you could trace, for example, that a particular influential tweet pumping a token was actually auto-generated by agent X (and if that agent is known to be backed by a hedge fund, you might view it differently). “Proof-of-humanity” might be less important when humans become minority actors online; instead proof-of-AI-origin and quality could be more crucial .
The regulatory perspective ties in here: one of the appeals of mixing blockchain with AI is creating trust in AI so it can be used in high-stakes scenarios like finance or healthcare. People might be okay with an AI diagnosing them if there’s a tamper-proof log of what data it looked at and a guarantee it was an approved model version (preventing adversarial tweaking). Nader Dabit’s quote encapsulates it: “AI is powerful — but verifiable AI is transformative… would you trust it with your mortgage or medical results? The trust gap is the barrier.”  . By closing that gap with blockchain verification layers, we could unlock those uses.
In summary, data and AI provenance is about making AI’s actions in Web3 transparent, attributable, and reliable. Techniques like on-chain logging, ZK proofs, and fully on-chain agents are different ways to achieve that. It’s still early (few agents actually implement zero-knowledge proofs of their outputs as of 2025), but the trajectory is clear: if we’re handing more economic power to AI agents, we’ll demand they prove themselves just as blockchains demanded proof from participants (work, stake, etc.). Perhaps we’ll see a “Proof-of-AI” consensus emerge for specialized chains where AI computations themselves are validated like blocks – there are already experimental consensus protocols named Proof-of-Useful-Work (for AI model training) and Proof-of-Intelligence.
Regulators have taken note of the agentic web’s rise, and early 2025 has been marked by intense discussion on how existing laws apply to AI agents and what new rules might be needed  . The challenges span multiple domains: securities law, financial licensing, consumer protection, data privacy, and even the legal status of autonomous entities.
In the United States, a key question is: if an AI agent is performing a regulated activity, who (or what) is on the hook legally? For example, if an agent gives investment advice or manages a portfolio, does that trigger the Investment Advisers Act? Legal consensus seems to be “yes, the activity is what matters, not who/what does it”. So if it walks and talks like an investment adviser, using an AI agent doesn’t exempt you from registration – the responsibility attaches to the humans or company behind the agent  . This means teams deploying, say, a robo-advisor DeFi agent might need to register or at least ensure compliance as if they were a traditional advisory firm. Similarly, if an agent is trading assets on behalf of users, could that require a broker-dealer license or similar? U.S. regulators in Q1 2025 hadn’t issued formal guidance, but they hinted strongly that “we will look through the AI to the people deploying it”  .
There’s talk about new licensing frameworks specifically for AI-driven services. Some lawyers suggest a “Level 5 autonomy” threshold: if an agent can make significant financial decisions without immediate user approval (fully autonomous), maybe it should require a new type of license or at least notice to regulators  . It parallels how self-driving cars are prompting new regulations for autonomous vehicles. In finance, an AI managing money might need an “AI fiduciary” classification. But nothing concrete exists yet, so in the interim regulators use existing laws: e.g., the U.S. SEC could charge a DeFi AI operator for unregistered securities trading or advice if users lose money and it looks like the agent was effectively an unregistered fund.
Liability is another thorny issue. If an AI agent causes a financial loss or breaks a rule, who is liable? Is it the user who deployed it (under the legal principle that an agent’s actions are your actions if you authorized it)? Or the developers who created it? Or the platform hosting it? U.S. electronic transaction law (UETA) suggests that actions by a “duly authorized” software agent count as the principal’s actions . That implies if I run an agent to trade and it screws up, I am liable. But UETA was meant for simple automation like auto-bill pay, not an AI that might learn and do unintended things. If the agent’s decision was unpredictable (even to its creators), is the user still on the hook? Early discussions lean towards expecting companies deploying agents to implement oversight and kill-switches, because they might be held responsible if the agent acts negligently or illegally  . For example, if a lending agent accidentally engages in insider trading (maybe it read some leaked info online and traded on it), could the platform be charged for that? This uncertainty is making legal departments very nervous.
Europe is addressing AI broadly with the EU AI Act (entered into force 2024, compliance obligations kicking in over the next couple years). The AI Act is a risk-based framework: AI systems are classified from minimal risk (chatbots) to high risk (like AI in finance, hiring, law enforcement). Financial services AIs likely fall under high-risk, meaning the providers have to implement risk management, transparency, human oversight, etc. . An autonomous DeFi agent might well need to comply by providing documentation on the model, having a way for users to appeal decisions, and logging activity. The Act doesn’t specifically mention crypto agents, but it will apply if the service is offered in EU. Also, the EU’s Markets in Crypto-Assets (MiCA) regulation (effective 2024/2025) could indirectly affect agents – e.g., if an agent issues a token or performs automated market making, those could be regulated activities under MiCA requiring licensing (as like a crypto asset service provider).
What about Asia? It’s a mixed bag: Singapore, for instance, is known to be forward-thinking. MAS (Monetary Authority of Singapore) has published principles for Fairness, Ethics, Accountability, and Transparency (FEAT) in AI use in finance . They might expect that if a bank or fintech in Singapore uses AI agents, it follows those principles (e.g., the AI decisions should be explainable to customers). Singapore is also implementing new regulations for crypto that take effect mid-2025, which include stricter licensing – any Web3 project dealing with tokens must fit into those frameworks . One could foresee MAS requiring that if customer funds are managed by an AI, the company must disclose that and meet certain audit standards.
China has taken a heavy approach to AI content regulation (like requiring government approval for generative AI models and mandating AI-generated content be labeled). While China’s crypto stance is restrictive (they banned crypto trading), they are investing in blockchain heavily for other uses. If agentic web concepts enter those permissioned blockchains, the AI likely will be tightly controlled and censored by design. Other countries like Japan are exploring Web3 and AI innovation, possibly with sandboxes where some rules are relaxed to foster growth.
One interesting point: legal personality for AI agents. Some futurists ask if advanced autonomous agents could be treated like legal entities (e.g. an AI gets some form of corporate personhood). The current consensus: we’re far from that, and regulators are reluctant to dilute accountability by blaming an AI (you can’t jail an AI or fine it unless someone pays on its behalf) . So at least in 2025, an AI agent is not a person under the law; it’s a tool, and responsibility traces back to natural or corporate persons involved.
Privacy and data protection laws also come in. If an agent is processing personal data (maybe an identity agent scanning IDs), GDPR and similar laws apply – meaning the operator must handle user data properly, even if an AI is doing it. And if an AI agent makes decisions that significantly affect someone (like denying a loan), GDPR’s right-to-explanation might require providing a rationale.
Regulators in various countries are also thinking about AI in trading. If AI agents manipulate markets (intentionally or unintentionally), how do existing laws on market abuse cover that? If a million agents coordinate (or one agent with a million instances) to pump a token, is that illegal manipulation and who to charge? Possibly the creators if intent can be proven, but these scenarios will test enforcement capabilities.
Overall, the regulatory outlook is one of “existing laws mostly apply, but we might need tweaks”. In the U.S., don’t expect a free pass – if anything, the SEC, CFTC, etc. are more likely to clamp down on autonomous financial apps if they see consumer harm. Europe will enforce transparency and risk mitigations via the AI Act. Asia will vary, with some hubs trying to attract AI+Web3 projects with clearer guidelines (e.g., Hong Kong’s recent overtures to crypto AI startups with regulatory sandboxes).
For developers and organizations, the safe approach is to assume full accountability: register or license if you’re doing regulated activities, include human oversight or throttles to prevent crazy AI behavior, and disclose the use of AI clearly to users. Also, work with regulators in shaping new rules – 2025 likely will see the first cases and enforcement actions that set precedents for AI agents (the first lawsuit involving a DAO’s AI agent messing up, etc.).
For non-developers, interacting with autonomous agents can be intimidating. If the UX is not improved, mainstream adoption will stall. Current challenges include: complex setup, lack of trust/intuitiveness, and recovery from agent errors.
Onboarding & Control: Right now, setting up an AI agent might require deploying a contract, running a script, configuring API keys – far beyond the average user’s ability. Projects like Pearl (Olas’s app store) aim to simplify this to a few clicks . User tells Pearl “I want a trading agent,” stakes some tokens, and it’s live. That’s promising. Similarly, Coinbase’s AgentKit suggests future wallet interfaces where spawning an agent is as easy as adding a new contact . Account abstraction (ERC-4337) helps here, since a smart wallet could let an agent pay for its own gas using the user’s funds in a controlled way, meaning the user doesn’t have to babysit transaction signing. Already, smart contract wallets (like Argent or Safe) allow setting transaction policies – e.g., “this address (the agent) can transact up to 0.1 ETH per day” – giving users a safety net. We need those guardrails widely available so users feel comfortable delegating.
Interface: How do users give instructions to or get updates from an agent? The ideal is natural language. If I could just tell a DeFi agent, “Manage my $5k with low risk, target 5% APY, and keep $500 liquid for emergencies,” and then just chat occasionally, that’s a win. Efforts are on to integrate conversational interfaces in wallets and dApps . We might see wallet chatbots: you ask “Hey wallet, how’s my portfolio doing?” and the agent replies in plain English and even suggests adjustments. This ties in with LLMs being the UI – the agent might incorporate a GPT-like model to talk to the user, while a more specialized model or logic handles the on-chain actions. It’s tricky to ensure the conversational part doesn’t hallucinate or mislead (imagine the agent UI says “All good!” when actually funds are lost due to a bug – user would be furious). So aligning the user-facing language with actual on-chain state is important (possibly by having the agent fetch data from chain and include it in its response to avoid hallucination).
Transparency vs Simplicity: Users want simplicity, but with autonomous agents controlling assets, they also need transparency to trust them. Striking that balance is a UX challenge. Providing a detailed log of every action and its rationale is reassuring to power users, but information overload to casual ones. One approach is multi-layered UX: a simple dashboard saying “Your agent is doing X, Y, Z, performance is +2% this week” for normal use, and an expandable “audit mode” where advanced users can drill into transactions and maybe even see the decision logic or data (perhaps a visualization of the agent’s neural network attention, etc., though that might be far-fetched for average Joe). Some projects like Questflow focus on orchestration and monitoring tools for agents – essentially dashboards to manage swarms of agents and visualize their processes  . Those might evolve into end-user tools too.
Trust and Safety: Handing over keys is scary. Users currently might use read-only tools (like a portfolio tracker) but balk at ones that can move money. Two things help: insurance and trial modes. We might see insurance providers cover losses from certain certified agents (for a fee), giving users peace of mind. Or agent platforms themselves could include a guarantee (like some funds reserved to compensate for failures – e.g., if an agent unexpectedly loses funds due to a bug, users get partially reimbursed). For trial, maybe users can let an agent simulate actions first (“shadow mode”), showing what it would have done without actually doing it. After a month if they’re happy, they switch it to live mode. This lowers the barrier as they can watch it “play” with ghost funds.
Intervention and Overrides: UX must allow the user to pause or stop an agent easily if something looks wrong. A big “panic button” essentially. And also to tweak parameters (“be less aggressive” slider). It’s similar to how autopilot in cars requires an easy way for drivers to take back control. If an agent sees an arbitrage but the user notices it might be a glitch, they should be able to say “don’t do that trade” or set constraints the agent must obey (like do not trade Token X, or keep at least 20% in stablecoins). Exposing these controls simply is hard – too many knobs and it’s confusing, too few and the user can’t steer the agent to match their preferences. One possible UI is “risk profiles” or templates – e.g., pick conservative/moderate/aggressive for a trading agent, which correspond to underlying parameter sets. Another is goal-oriented input: user says the outcome they want (“I need 1 ETH by year-end to pay tuition”) and the agent adjusts strategy accordingly – behind the scenes, maybe switching from risky yield farming to stable saving as it nears the goal.
Learning and Support: Because this is new tech, user education is part of UX. People need to understand at a conceptual level what the agent will do. Perhaps an initial tutorial where the agent introduces itself: “Hi, I’m AutoYield. I will move your funds between various DeFi pools. You can always see what I did in the Activity tab. I charge 5% of profits as fee. Shall we begin?” – a friendly onboarding agent that sets expectations. And ongoing support: maybe a built-in Q&A (“Why did you move my funds from Aave to Compound?” – user asks, agent explains “Because Compound’s rate became higher ”). If the agent can explain itself in plain terms referencing real data, that builds trust and understanding.
Case of failure: If something goes wrong – say a bug or exploit causes loss – how does the user find out and what happens? The UX should immediately notify, explain and guide next steps (“We lost 10% due to a hack on Protocol X. This was beyond the agent’s control. We’ve paused operations. Click here to withdraw remaining funds.”). Handling bad scenarios gracefully is key to not losing the user forever.
We should also mention performance and latency in UX. Web3 actions can be slow (waiting for block confirmations). Agents might mitigate that by operating on L2s for speed, but if an agent is conversing with a user and needs to fetch on-chain data or execute a trade, there might be a delay of seconds. The UI should have proper loading states (“Agent is securing the best rate for you…”) instead of leaving the user hanging.
So, while current agent interfaces are rudimentary, by the end of 2025 we expect a leap in polish. The goal is an experience where using an agent feels as easy as using a robo-advisor app or a voice assistant, with added superpowers like transparency of funds and community-vetted strategies. Web3 always had a UX problem (private keys, confusing wallets) – ironically, AI agents could solve some of that by abstracting complexities. Users might not deal with signing every tx; their agent handles it under the hood with security policies. In fact, one could say agents are poised to become the new UX layer for Web3  , turning clunky contract interactions into smooth goal-oriented experiences. The projects that crack this will be the ones to bring agentic web to the masses.
Having covered the landscape of challenges, let’s turn to the road ahead: how might the agentic web evolve in the coming years, and what metrics and trends should we watch?
The rise of AI agents in Web3 is on an exponential trajectory. By late 2025, we expect to see significant growth in the scale of agent activity, new monetization paradigms taking hold, flourishing agent-to-agent markets, and strategic shifts by blockchain platforms to embrace this new wave. Here’s an outlook on key trends and projections for the ecosystem:
All indicators point to agent-driven activity becoming a sizable chunk of Web3 usage. As noted, VanEck analysts predict over 1 million on-chain AI agents by end of 2025  . These won’t all be distinct super-intelligences; many could be simple strategy bots or NPCs, but it signals sheer volume. In terms of network load, this could mean a significant share of transactions are initiated by agents rather than humans. Some early data suggests this shift: by Dec 2024, agents were already generating millions in weekly revenue from on-chain activities . If each agent is doing even a handful of transactions daily, a million agents could contribute several million transactions per day across chains.
We can expect Ethereum and Layer-2s to measure agent activity as a new metric, like “gas used by agent contracts” or “% of transactions tagged as agent-driven”. Already, agent-oriented protocols like those on Base and Arbitrum are growing. Coinbase’s Base, for example, actively courted agent developers (e.g., the Virtuals platform, AgentKit) and could see a lot of usage spike there. Solana, with its high throughput, might attract agents for trading and gaming tasks that need speed (there’s been talk of Solana powering rapid in-game agent actions).
In DeFi, agents could boost Total Value Locked (TVL) indirectly: if agents drive new strategies, more capital might flow in. The VanEck report enumerated that the current focus of agent building was DeFi and that it’s expected to transcend purely financial tasks into social and gaming as well . If a good fraction of the projected $200B DeFi TVL (VanEck’s 2025 prediction ) is managed by AI agents, it underlines how integrated they’ve become.
Another metric is trading volume by AI. Could we see, by late 2025, that X% of DEX trading volume is executed by AI agents? Quite plausible – already a lot of on-chain trading is bot-driven arbitrage. With more sophisticated agents, they might dominate liquidity provision and arbitrage. Traditional markets have >50% high-frequency trading; crypto could mirror that with AI agents as the primary liquidity actors.
Daily Active Agents (DAA) is a new metric some platforms like Olas publish (they showed growth in “Daily Active Agents”) . Instead of daily active users, how many agents did something today? This might surpass human DAUs in some dApps. For instance, a lending platform might have 1,000 human users and 10,000 agent users (managing those humans’ funds in slices). We’ll likely see dashboards on Dune Analytics tracking agent counts, agent transactions, etc.
All that growth is contingent on user adoption and trust continuing to rise, which depends on solving the challenges we described. If a major exploit or scandal happens (like an agent causing a huge loss), that could temporarily slow adoption. But if things go relatively well, 2025 could indeed be the year agents go from curiosities to commonplace in crypto.
The business models around agents are innovating quickly. Beyond just launching a token, we’re seeing concepts like:
When agents pay each other, new monetization surfaces: maybe an agent that creates valuable data (like a super-accurate price predictor) sells its signals to other agents. Data unions of agents could form, where they pool data and sell aggregated insights. The possibilities here are barely explored.
A driving concept is “co-ownership”: the people involved in creating and running the agent all share in its upside. Autonolas’ co-founders often mention “co-own AI”  – meaning you can own a piece of the agent economy. This could lead to new work opportunities: developers might prefer writing an agent that, if widely used, provides them passive income, rather than working for salary. Operators might choose which agents to run based on profitability, etc.
One risk: if an agent economy becomes very lucrative, it might attract centralization or hostile takeovers (like someone buying up majority of agent tokens or nodes). The communities will need to guard decentralization to keep the playing field open.
We already touched on it, but it deserves emphasis: by 2025 we anticipate vibrant agent marketplaces and direct agent-to-agent commerce. This is a step beyond just marketplaces where humans pick agents – here, agents themselves find and utilize other agents’ offerings.
For this to work, discoverability is key. Google’s A2A protocol initiative suggests something like an Agent Yellow Pages or search engine . Perhaps a DApp or chain dedicated to agent registry where each agent lists its API, price, and credentials. Agents can query this registry to find collaborators. Projects like Fetch.ai and others have long talked about agents negotiating services in open markets (their vision of autonomous economic agents, AEAs, was along these lines). We’re finally nearing the point where that is feasible on a broader scale thanks to standardization efforts.
One can imagine scenarios like: a complex DeFi agent breaks a task into pieces – one piece needs a sentiment analysis of crypto news, another needs a prediction of interest rates, another needs to execute trades across chains. Instead of doing all itself, it queries marketplaces: finds a sentiment analysis agent with good reviews and calls it (paying maybe $10 in crypto), gets the result; finds a “rate oracle agent” and gets a forecast (paying a small fee or maybe giving a small fraction of profit); then uses a cross-chain execution agent to do trades on multiple networks, splitting profits accordingly. These agents might not even know the human at the top of the chain, they just fulfill their narrow role and get paid.
This is basically automated B2B commerce but the businesses are bots. It will require robust identity and reputation: an agent will only hire another if it trusts it to deliver quality. Reputation systems might involve on-chain ratings, performance audits, etc. (This loops back to Proof-of-AI – an agent might only hire another if it can verify its claimed capabilities).
Agent marketplaces could also trade agent components. For example, selling a strategy or a skill as a plugin that agents can incorporate. Similar to app stores selling libraries to developers, but here an agent could dynamically buy a skill when needed (“Oh I need to do image recognition, I’ll pay to use this Vision plugin for an hour”).
One existing analog is API marketplaces (RapidAPI, etc.), which let developers pay per API call to third-party services. Agent-to-agent markets are like that, but fully automated and potentially decentralized (payment in tokens, service discovery via smart contracts).
The implications are huge: we might get an “economy of AIs” where pricing and supply-demand dynamics drive the evolution of agents. If data becomes expensive, agents will try to be more efficient; if too many agents do the same thing, their service price drops, etc. It’s a bit sci-fi, but early rudimentary versions are imminent (some exist in test forms).
From a human perspective, one could be mostly removed from these interactions – you just see your agent spent $0.50 to get some data and made an extra $5 profit thanks to it, which is fine. It’s like your AI having an expense account to optimize outcomes.
As the agent trend accelerates, base layer protocols (Layer-1 and Layer-2 blockchains) will adapt to better support these use cases – partly to capture the activity, partly to solve technical needs.
One clear shift is an embrace of account abstraction. Ethereum’s move with ERC-4337, and similar efforts on other chains, allows smart accounts that can be controlled by code and have flexible transaction validation. This is extremely useful for agents: it lets them have wallets with custom logic (e.g., social recovery, spending limits) and even sponsored gas (an agent could pay gas with an ERC-20 or be sponsored by another account), smoothing UX. L2s like StarkNet natively use account abstraction, which is great for deploying agents with custom authorization schemes (like multi-sig or multi-owner agents). We’ll see more chains integrating these features or improving them (e.g., reducing the overhead of AA transactions).
Scalability is another consideration. If agents are doing lots of small transactions, high throughput and low fees are musts. This suggests agent activity will gravitate to Layer-2s or high-performance L1s. We might see an L2 branding itself explicitly as “Agent Chain” optimized for AI agent execution – perhaps with shorter block times (for faster reaction), special opcodes or precompiles for AI tasks (like BLS library for verifying AI proofs quickly), and economic parameters tuned for machine load rather than human load (maybe different fee markets or gas pricing if transactions come in surges due to synchronized agent behavior).
There’s already specialized chains in the works: e.g., Kite AI claims to build an EVM-compatible L1 with “Proof of AI” consensus , meaning the consensus itself involves AI processing contributions. Fetch.ai has its own chain geared towards multi-agent systems with an AI-friendly bent. Sei Network is a new L1 targeting high-frequency trading (which could include AI trading bots) with fast finality. As these gain attention, mainstream L1s like Ethereum, Solana, Binance Chain will ensure they don’t miss out on agent-driven volume by possibly offering grants, building native SDKs for agents, or even integrating AI oracles at the protocol level.
Another shift might be in storage and data availability. Agents may produce a lot of off-chain data (like logs, learned parameters). Decentralized storage solutions (Arweave, Filecoin) might be more tightly coupled with L1s to store agent data. Perhaps L2s will integrate with a decentralized database (like how some L2s talk about off-chain data availability sidecars). A network could differentiate itself by being “the chain where your agents can store and retrieve big data cheaply and verifiably.”
Gas market dynamics could change when many agents compete. If hundreds of arbitrage agents see the same opportunity, they might spam the network to grab it (similar to today’s MEV bots bidding priority gas). This could increase network fees or congestion. Chains might implement more sophisticated scheduling – maybe a built-in fair ordering for certain agent transactions, or separate lanes for human vs agent tx. It’s possible we’ll need new solutions for MEV (Miner Extractable Value) in an AI world, because agents will be both generating and vying for MEV. Perhaps more adoption of protocols like Flashbots or auction systems to civilize the competition.
Governance of base layers might also consider AI input. Conceivably, an L1 might use AI agents to simulate outcomes of protocol changes or to manage certain parameters algorithmically. Or even give AI delegates a voice in governance (as odd as that sounds, there was talk of DAOs with AI governors).
And let’s not forget cross-chain: Agents that operate across multiple L1s will push for better interoperability. This could benefit protocols like Axelar, Cosmos IBC, Polkadot, etc., as agents will use cross-chain messaging to arbitrage or coordinate. L1s that make it easy for an agent to act on many chains from one place will see more agent usage. We already see, for example, Axelar supporting an interchain AI demo where an agent on one chain queries something on another.
Finally, L1 strategies: For Ethereum, if agents drive lots of transactions, that’s good for fee revenue, but if it all moves to L2, Ethereum becomes the settlement backstop mainly. Ethereum might prioritize features that make it the trust hub for agents (like data availability for proofs, robust oracle integrations). Other L1s might try to position as “our chain is where the most powerful AI agents live” possibly by running AI inference on validators (some wild ideas out there of integrating AI into consensus, though that’s speculative). It’s reminiscent of L1s optimizing for gaming or storage; now optimizing for AI is on the table.
In conclusion, the agentic web in 2025 is poised to significantly rewire how Web3 operates. We started with people interacting with smart contracts; we’re moving to agents interacting with smart contracts – and with each other – on behalf of people (and sometimes on their own behalf!). Technical architecture is solidifying, real use cases are proving value, and the ecosystem is rapidly addressing challenges around security, trust, and UX. The competitive landscape between Web3 and Web2 automation is clarifying why this movement is unique: it’s not just automation, it’s autonomy with alignment and ownership.
If the current trajectory holds, by the end of 2025 we might routinely see headlines like “DAO’s quarterly report prepared entirely by AI agents,” “80% of DEX volume driven by autonomous agents,” or “AI agent economy reaches $10B in on-chain revenue.” The precise numbers are less important than the trend: a growing portion of economic activity online will be executed by agentic AIs with crypto as their native platform for value and trust.
Yet, this is the dawn of a new era – much like the early internet, it comes with uncertainties. Ensuring these agents are aligned with human values and prosperity will be the ultimate test. Web3’s ethos of open, transparent, and community-governed systems provides a hopeful framework to manage this evolution. In the Rise of the Agentic Web, those that effectively blend AI’s capabilities with Web3’s principles of decentralization and composability are likely to shape the future of both technology and society in profound ways. Buckle up – our new robotic colleagues are just getting started, and they’re recalibrating the very wiring of the web before our eyes.  
Need to architect, audit, or monetize your own agent fleet? Mozaik Labs designs secure agent frameworks, stitches them into modular chains, and keeps them battle-ready with real-time monitoring. Let's build the Agentic Web together.
Lead AI Researcher at Mozaik Labs, focusing on autonomous agents and their applications in Web3. Previously worked on AI systems at OpenAI and Google DeepMind.
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