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AI Agents in the Cryptocurrency and Blockchain Space

April 5, 2025ai

Artificial intelligence is increasingly intertwined with blockchain, giving rise to AI agents that autonomously perform complex tasks in crypto trading, decentralized finance (DeFi), and development. This report surveys the 2024–2025 landscape of AI agents in crypto, focusing on technical architectures and applications. We cover AI Trading Agents (for trading and portfolio management), Developer Agents (for smart contract coding and auditing), emerging Agentic Frameworks for on-chain agents, and the use of Model Context Protocols (MCPs) to connect AI models with blockchain data and services. Each section provides technical depth, examples, and recent innovations in this fast-evolving field.

AI Trading Agents

AI trading agents are autonomous programs that analyze market data, make decisions, and execute trades or DeFi actions – often without human intervention Unlike traditional crypto trading bots that follow fixed if-then rules, AI agents leverage machine learning and agentic reasoning to adapt strategies in real time. For example, instead of simply buying when Bitcoin drops 5%, an AI agent might assess multiple factors (market sentiment, liquidity shifts, whale activity) to decide if a dip is a genuine opportunity or a trap. This ability to process vast streams of on-chain data, news, and social sentiment and adjust on the fly marks a leap beyond static bots

Architecture and Data Sources: AI trading agents typically incorporate several components: data ingestion pipelines, a decision-making engine (often an ML model or ensemble), and execution modules. They draw on diverse data sources: real-time price feeds, order book data, on-chain metrics (e.g. DeFi liquidity changes), and even unstructured data like news or tweets. Advanced agents use machine learning (including deep learning and reinforcement learning) to predict market movements or optimize portfolios using various techniques. Many also utilize natural language processing to gauge sentiment or detect narratives affecting crypto markets. This data-driven adaptability allows them to outperform rigid rule-based bots in volatile markets.

Agentic Reasoning and Capabilities: Modern AI agents can perform complex reasoning to plan trades or strategies. They do not simply react to one indicator but consider multiple variables and potential outcomes. For instance, an agent might reason about an upcoming protocol upgrade by reading developer posts and combine that with on-chain options data to anticipate volatility. Some agents employ large language models (LLMs) for higher-level decision logic, chaining together analysis steps (e.g. "If liquidity is moving to DEX X and Twitter sentiment is bullish on token Y, then allocate more capital to Y's liquidity pool."). This multi-step reasoning enables dynamic strategy shifts that were previously hard-coded or impossible for simpler bots.

Deployment and Integrations: AI trading agents operate in both centralized exchange environments and decentralized on-chain ecosystems. In centralized exchange trading, an AI agent runs off-chain (on a server) and connects to exchange APIs to execute orders. Standard libraries and protocols now simplify this integration. For example, the open-source CCXT MCP server provides real-time market data and trade API access to major exchanges like Binance, Coinbase, Kraken, KuCoin, and others. Through such interfaces, an AI agent can monitor order books and place orders across multiple exchanges seamlessly. In decentralized finance, trading agents often interact directly with smart contracts (for DEX swaps, yield farming, etc.) using on-chain transactions. Agents may be deployed as bots listening for on-chain events or integrated via smart contract wallets. With new standards like the EVM Model Context Protocol server, an AI agent can read blockchain state or send transactions on 30+ EVM networks programmatically, enabling direct participation in DeFi protocols.

Use Cases and Examples: Initially, trading agents focused on profit strategies (arbitrage, trend trading), but in 2024–2025 their roles have expanded across crypto finance:

  • Automated DeFi Portfolio Management: AI agents manage portfolios and liquidity positions across multiple DeFi platforms, constantly rebalancing to maximize yield. For example, the agent platform Griffain offers AI-driven portfolio allocation across yield farms, adjusting in real time to manage risk. Such agents analyze interest rates, liquidity pool rewards, and protocol health to shift assets to the most optimal venues for maximum returns. This dynamic yield optimization (sometimes termed "DeFAI" for DeFi AI) means users no longer have to manually chase yield — an agent can autonomously move stablecoins from a lending pool to a higher-yield farm after assessing gas costs and risks.

  • Risk Monitoring and Mitigation: AI agents help manage the significant risks in crypto markets. They monitor on-chain metrics and off-chain news to detect anomalies. For instance, an agent can watch a lending protocol's vaults and if it observes abnormal withdrawals or a governance attack, it can automatically withdraw funds or hedge positions before a potential collapse. Similarly, agents can track stablecoin peg metrics or liquidity crunches and take preventative action (e.g. unwinding a position if a stablecoin is depegging). This proactive risk management was not feasible with static bots that lacked real-time learning.

  • Autonomous Trading and Market Making: A number of AI agents execute trades on exchanges aiming to profit from short-term patterns or provide liquidity. These agents use predictive models trained on historical data and continuously refine their strategies. For example, the hedge fund Numerai leverages crowd-sourced AI models to predict market movements and execute trades, reducing risk exposure through ensemble learning. On decentralized exchanges, AI market-making agents adjust their liquidity provisioning based on predicted volatility, moving liquidity between pools or adjusting fee tiers to maximize fees earned while mitigating impermanent loss.

  • AI-to-AI Transactions: A milestone in 2024 was the first recorded cryptocurrency transaction carried out entirely by AI agents. In August 2024, Coinbase oversaw a fully autonomous trade where one AI agent's wallet used crypto tokens to purchase AI-themed tokens from another agent's wallet. This demonstrated "AI agents with wallets" in action – essentially AIs transacting with each other without human involvement. It underscores the concept that an AI agent can hold and transfer digital assets on behalf of a user or itself. As noted at the ETHDenver 2025 conference, "an AI agent with a wallet is a powerful thing" because it combines autonomous decision-making with direct on-chain execution.

  • Beyond Trading – DeFi and Governance: AI agents are branching into other crypto domains. Some agents now participate in DAO governance, automatically voting on proposals according to predefined guidelines or learned preferences. Others manage NFT portfolios by analyzing metadata and market trends to buy undervalued NFTs and sell at a profit. For example, agents leveraging SingularityNET's decentralized AI marketplace can perform NFT rarity analysis and valuation using on-chain data. In blockchain governance, specialized agents (e.g. a "Governator" agent) can represent a user's voting interests and cast votes on proposals while the logic and boundaries of its actions are enforced via smart contract (preventing it from going rogue).

Table 1: Examples of AI Trading Agents and Platforms (2024–2025)

Agent/PlatformFocusCapabilitiesActive On
Griffain (AI Agent Engine) with portfolio optimizationDeFi portfolio automationDynamic asset allocation across yield farms; risk-managed rebalancing based on real-time ML insights.Ethereum DeFi (various DEXes/yields)
Fetch.ai DeFi Agents for DeFi automationAutonomous DeFi tradingTrading and liquidity optimization; gas fee and risk management via autonomous economic agents.Cosmos-based chain; Ethereum bridges
Numerai (AI hedge fund) with risk managementMarket prediction & tradingEnsemble ML models predict asset prices; executes trades to maximize returns while minimizing risk.Centralized exchanges (crypto & stocks)
Solana AgentKit (SendAI) for forex trading with MCPOn-chain forex trading agentMCP-driven agent can swap stablecoins (USD⇄EUR) and deploy tokens on Solana; 40+ on-chain actions (transfers, swaps, etc.).Solana blockchain (on-chain DEX)
Coinbase AI Agents for autonomous tradingAutonomous CEX tradingTwo LLM-driven agents executed token trade with no human input; demonstrates AI-managed CEX wallets trading assets.Coinbase Exchange (Base L2)
DAO Vote Bots (Valory "Governator") for governanceDAO governance automationAgent holds governance tokens and votes per user-defined policy; ensures transparency and compliance via on-chain rules.Ethereum/Gnosis (DAO treasuries)
NFT Trading Agents (SingularityNET) for NFT automationNFT valuation & tradingAnalyzes NFT attributes and sales, estimates fair value; auto-bids on undervalued NFTs and relists for profit.NFT marketplaces (OpenSea, etc.)

Technical Note: These agents typically run off-chain (as cloud services or local daemons) for heavy computations, while signing transactions with keys to act on-chain. However, they are increasingly integrated with on-chain logic. For instance, Fetch.ai's agents can be deployed in a peer-to-peer network and coordinate via Fetch's blockchain, and Solana AgentKit runs an MCP server that bridges an off-chain AI model with on-chain Solana programs for seamless integration. This blending of off-chain intelligence with on-chain execution is a defining trait of crypto AI agents.

Despite their promise, AI trading agents also introduce new risks and challenges. Ensuring they operate safely (avoiding erratic behavior that could cause flash crashes) is critical. Some efforts involve constraining agents with smart contract-defined "rules of engagement," which we discuss later. Overall, by 2025 AI agents have moved from experimental to essential in managing the complexity of crypto markets across multiple applications, handling tasks at speeds and scales impossible for humans alone.

Developer Agents for Blockchain Development

Blockchain developers are tapping AI agents to automate and assist in smart contract creation, auditing, testing, and deployment. These developer agents range from coding assistants integrated in IDEs to autonomous auditors that scour contracts for vulnerabilities. The goal is to accelerate development while improving code quality and security in a domain where "code is law" and mistakes are extremely costly.

AI Code Assistants: Just as GitHub Copilot assists general programmers, crypto developers now have AI copilots specialized in Solidity, Vyper, and other smart contract languages. Large language models (LLMs) like GPT-4, Claude 2, and CodeLlama have been trained or fine-tuned on blockchain codebases to provide contextual code completions and suggestions. Recent evaluations (Aug 2024) found that Anthropic's Claude 3 and certain open models like DeepSeek Coder performed best for Solidity code completion tasks, even surpassing other general models in inserting correct code snippets. This means an AI assistant can autocomplete function arguments or suggest entire contract snippets (e.g. an ERC-20 implementation) with high accuracy. Developers can prompt these agents to generate a new smart contract from scratch given a description, and the agent will produce standardized, often security-hardened code. Some tools (e.g. "Solidity AI" or "SolidityGPT") provide chat interfaces where one can ask, "Write a Solidity function to distribute rewards proportional to staked amount," and the agent will output the code and even explain it. Such capabilities dramatically speed up development, although human review remains essential.

Automated Auditing and Security Analysis: Perhaps the most impactful use of AI for blockchain developers is in smart contract auditing and testing. Security audits traditionally take weeks of manual effort, but AI agents can perform a first pass in seconds. For example, ChainGPT's AI Auditor takes a contract as input and runs a suite of analyses based on the vast history of known vulnerabilities and exploits offering multiple benefits. It will highlight potential bugs (reentrancy, overflow, access control issues, etc.) and even logical flaws or gas inefficiencies. While not a replacement for professional auditors, it serves as a "pre-emptive security layer" to catch glaring issues early. The ChainGPT auditor supports multiple blockchains (Ethereum, BNB Chain, Arbitrum, Avalanche, Solana, and more) with cross-chain capabilities and can be invoked through a chat interface or API, making it easy to integrate into a developer's workflow. Beyond specific tools, academic research is advancing AI auditing: AuditGPT, introduced in 2024, used ChatGPT with specialized prompts to systematically verify smart contracts against Ethereum standards (ERCs). It broke down the auditing process into small tasks and checked 222 ERC rules, successfully identifying 418 rule violations with few false positives. Impressively, AuditGPT was shown to outperform human experts in catching certain specification violations, hinting that AI could augment or even automate compliance checks for contract standards. Another research prototype, SCareGPT, explored using a GPT-based assistant fine-tuned for security to detect vulnerabilities, illustrating the trend of domain-specific LLM auditors.

Development Workflow Integration: Developer agents are being integrated into the tools developers already use. For instance, plugins for popular frameworks like Hardhat or Foundry can leverage an AI to generate unit tests for a given contract or suggest fixes for failing tests. AI can also assist in deployment scripts: given a description of a deployment scenario, an agent could generate the script to deploy contracts in the correct order with proper initialization parameters. Model Context Protocol connectors play a big role here (detailed later). An example is the Thirdweb MCP server, which exposes capabilities to read contract ABIs, deploy contracts, and send transactions on over 2,000 blockchains via Thirdweb's SDK. A developer-focused AI agent can use this to, say, "deploy an ERC721 contract to Polygon" in one step. Similarly, an MCP integration with Semgrep (a static analysis tool) allows an AI agent to run security scans on code and interpret the results – effectively the agent can combine pattern-based static analysis with its own reasoning to double-check a contract for vulnerabilities.

Many blockchain IDEs and repositories now embed AI assistance. For example, when writing a Solidity function, an AI agent might automatically suggest improvements (like using unchecked for gas optimization or indicating where to add require statements for input validation). Companies like Trail of Bits have even created custom evaluation harnesses (e.g. CompChomper) to assess and improve how well AI coding models handle Solidity for various models, reflecting the demand for better AI support in blockchain languages. By late 2024, the consensus was that local fine-tuned models can nearly match the big proprietary models for Solidity tasks, and that model size and specialization matter – e.g. larger specialized models (like DeepSeek's 6.7B model) outperformed CodeLlama which lacked Solidity training.

On-Chain vs Off-Chain Operation: Almost all developer AI agents run off-chain, given the heavy computation of ML models. They interface with blockchain data via RPC calls or oracles. However, there are interesting experiments in on-chain AI execution. Projects like SingularityNET have explored running certain AI model inference fully on-chain (for example, using Ethereum + Layer 2 for computation). The idea would be to have a smart contract that can evaluate an ML model's output given some input, ensuring the AI logic itself is transparent and decentralized. In 2025 this is still in early stages due to cost and technical constraints, but progress in zero-knowledge proofs and specialized chains (e.g. Cortex or Fetch.ai's chain) could eventually allow AI agents (or their critical components) to live on-chain. For now, developer agents act as off-chain bots with permissions: when they need to affect on-chain state (like deploying a contract or updating a parameter), they do so by sending transactions through a wallet they control.

Examples of Developer AI Tools: The table below highlights some notable AI tools aiding blockchain developers:

Table 2: AI Developer Agent Tools and Frameworks

Tool/AgentPurposeCapabilitiesIntegration
Solidity Copilots (e.g. Claude, CodeGPT)Code generation & completionSuggests code snippets, completes functions, explains code and docs; fine-tuned on smart contract patterns.VS Code, Remix via extensions
ChainGPT Auditor for security analysis with multiple benefitsSmart contract audit botScans code for vulnerabilities and bugs using AI knowledge base; supports multiple chains; returns a report in seconds.Web app & API (off-chain)
AuditGPT (Research) for contract verificationCompliance verificationUses ChatGPT with structured prompts to check if contracts adhere to ERC standards; finds specification violations efficiently.Offline analysis (post-deployment)
AI Unit Test GeneratorsTesting automationGenerates unit test cases for smart contracts by analyzing function logic and potential edge cases.Hardhat/Foundry plugins (off-chain)
Semgrep MCP Agent for code analysisSecurity static analysisRuns Semgrep rules on contract code and lets the AI interpret and summarize issues found, combining pattern matching with AI advice.MCP server + local AI client
Thirdweb MCP for deploymentDeployment & on-chain accessEnables AI to deploy and interact with contracts on 2k+ chains via Thirdweb SDK (query contract state, send txns, etc.).MCP server + AI client (Claude, etc.)
BuilderChain AI (MCP integrated) for workflow integration with industry applicationsWorkflow automation (construction domain)Example of domain-specific use: AI agent uses MCP to connect project management tools, financial platforms, and on-chain escrow contracts, automating multi-party workflows.Off-chain agent using MCP to tie into on-chain apps

Note: The last example (BuilderChain AI) demonstrates that these AI developer agents are not limited to writing code—they can coordinate entire DevOps workflows in a decentralized context. By leveraging MCP, an AI agent can integrate with version control (GitHub), CI/CD pipelines, and blockchain deployment scripts all through a unified interface for streamlined operations. This hints at a future where a project manager could tell an AI agent to "deploy the latest smart contract and update the frontend", and the agent could handle everything from running tests, deploying to an Ethereum network, to updating a web app—bridging Web2 and Web3 tasks.

Agents.Land (Oraichain) and AI Oracles: Another cutting-edge framework is Agents.Land, launched in early 2025 by Oraichain Labs. Agents.Land is a decentralized, no-code platform on the Oraichain network that lets users create and deploy AI agents easily. It focuses on user-friendly creation (even offering one-click deployment) and combines AI Oracles with blockchain to ensure trust. An Agents.Land agent can do things like manage a crypto portfolio, run an automated trading strategy, or even act as a virtual customer service rep, all on-chain. What sets it apart is the emphasis on security through Trusted Execution Environments (TEEs): Agents.Land uses TEEs to securely handle private keys and sensitive operations, preventing even the agent owner from tampering with certain actions. This means an AI agent could autonomously manage funds, but the keys and critical logic are in a secure enclave, reducing the risk of hacks or rogue behavior. Additionally, Oraichain's AI Oracle mechanism validates AI outputs before they trigger on-chain actions. For example, if an agent generates a price prediction or decides to execute a trade, the AI Oracle can require the agent to pass certain test cases (or threshold conditions) to confirm the output is reasonable before allowing the transaction. This is akin to a fail-safe or circuit-breaker for AI decisions on-chain. Agents.Land also touts interoperability: agents can interact with Web2 APIs and Web3 dApps alike for various applications. This has enabled scenarios like agents that autonomously run entire businesses – e.g. monitoring e-commerce data (Web2) and managing crypto treasury and NFTs (Web3) under one roof. While still new, Agents.Land demonstrates the trend of integrated AI + blockchain platforms where non-experts can deploy sophisticated agents with built-in trust guarantees.

Virtual Worlds and AI Avatars (Virtuals Protocol): An interesting niche of agentic frameworks is exemplified by Virtuals Protocol (often called Virtual Protocol), which gained popularity on Coinbase's Base L2 network in 2024. Virtuals Protocol is essentially an AI agent launchpad for gaming and entertainment – it allows users to create AI-driven virtual characters or NPCs that can be owned, traded, and interacted with on-chain. Each agent (like the famous Luna AI idol) has its own token and can perform actions like chatting with users, generating content, or managing virtual assets. The platform achieved a high profile when its native token $VIRTUAL spiked in late 2024 with a multi-billion market cap, fueled by the hype around AI agent "memecoins." Technically, Virtuals and similar projects (e.g. Pump.fun or aiPool using Phala Network TEEs) highlight a framework where many AI agents run in parallel, each as a sort of NFT/DAO hybrid: the community can contribute to training the AI or guiding its personality, and they collectively own the agent via tokens. This decentralized ownership of AI models is a novel concept – think of it as an AI DAO, where the agent is both the product and an autonomous entity. While some of these projects have been speculative, they have pushed innovation in how to securely run many agent instances. For example, using Phala's TEE-based cloud to run AI agents ensures the agent's code and model weights are locked and cannot be rug-pulled or altered maliciously by the creators. In summary, frameworks like Virtuals Protocol expand agentic tech into the social and gaming realm, showing that not all agents are financial – some are interactive companions or game players on-chain.

Other Notables: Beyond the above, there are additional frameworks and tools: SingularityNET (a decentralized AI marketplace) is evolving to support on-chain AI services, letting agents use AI algorithms in a decentralized way and even run partially on-chain to reduce oracle needs. Projects like Aletheia and HiveMind (hypothetical names representing decentralized agent collectives) are in development, focusing on coordinating swarms of agents to perform large-scale analytics on blockchain data. We also see mainstream tech companies recognizing the trend: Salesforce's 2025 offering allowed AI agents to interact across traditional apps, and similar ideas are migrating to blockchain integrations (though Salesforce's was not on-chain, the pattern of multi-system agents is relevant).

Key Features Across Frameworks: Despite different focuses, these frameworks share common technical themes:

  • Agents with Wallets: Each agent typically has its own blockchain account (or smart account), enabling it to hold and transfer digital assets and to have an on-chain identity. This is crucial for autonomy – an AI can then truly act in the economic sense (pay fees, receive payments, vote in governance, etc.).

  • On-Chain Control and Logging: Smart contracts are used to set boundaries (what actions are allowed) and to log important agent actions for transparency. This creates an immutable audit trail of agent behavior, which is vital for trust when agents manage value.

  • Multi-Agent Communication: Many frameworks implement messaging protocols so that agents can talk to each other or to services. For example, Fetch.ai agents discover each other via a blockchain directory and then communicate P2P for complex tasks (like a logistics agent hiring a delivery agent). In other cases, agents coordinate by writing to a common smart contract (e.g. one agent posts a task on-chain that another can pick up).

  • Human-in-the-Loop and DAOs: The most advanced setups allow human stakeholders to guide or intervene in agent operations via DAO mechanisms. Autonolas's co-owned agents and Virtuals's token-governed agents exemplify this, turning agent management into a community affair where upgrades or parameter changes are voted on, and multiple parties can benefit from an agent's success.

Table 3: Notable AI Agent Frameworks (2024–2025)

Framework / PlatformDescriptionKey FeaturesMaturity
Autonolas (Valory Olas) for agent railguards with on-chain governanceMulti-agent system with on-chain governance. Co-owned AI services controlled via smart contracts (finite state machines as "rails"). Deployed on Gnosis Chain.Smart account wallets for agents; on-chain enforced rules; agents for trading, prediction markets, governance with 1M+ tx performed.Live (2024) – High usage on Gnosis
Fetch.ai (AEA framework) for DeFi tasks and optimizationDecentralized agent network on Fetch blockchain. Agents discover & communicate to perform tasks (trading, data, IoT). Emphasizes autonomous economic transactions.Agent communication protocol; blockchain-mediated marketplace; tools for DeFi (trading, gas optimization) and beyond.Live (2019+, major expansion by 2024)
Eliza (ai16z open-source) with latest upgrades for multiple modelsOpen-source LLM agent framework ("agents for everyone"). Modular design, multi-agent chat rooms, plugin integrations for tools and platforms. Growing ecosystem on crypto Twitter.Supports any LLM (GPT-4, Llama2, etc); easy integration with social media and Web3 APIs; vibrant community building agent demos (Twitter bots, on-chain interactions).Launched 2024 – v1 popular, v2 in development
Agents.Land (Oraichain) for autonomous operations with TEE securityNo-code platform for AI agents on Oraichain. Targeted at creating autonomous agents for DeFi, NFTs, and Web2 tasks with strong security guarantees.TEE-based secure agent execution; AI Oracle validation of outputs; one-click deployment; agents interact with Web2 & Web3; reward mechanisms for agent performance.Launched 2025 (Early adoption)
Virtuals Protocol (Base) for gaming and NPCsAgent launchpad for AI avatars/NPCs. Users create and co-own AI-driven characters (with tokens). Focus on gaming, virtual influencers, content creation.Each agent has a token and on-chain personality; community training input; uses Base L2 for fast transactions; modular smart accounts allow shared control with users.Launched 2024 – High growth then stabilization
SingularityNETDecentralized AI marketplace and network (multi-chain). Now exploring on-chain AI execution and autonomous AI DAOs.Agents can publish AI services; on-chain token payments; research into fully on-chain model inference to remove centralized oracles.Live (2018+, expanding features in 2024)

These frameworks indicate that the building blocks for an "AI agent economy" are falling into place. By combining blockchain's trust and transparency with AI's autonomy and intelligence, they enable new classes of applications. For example, one can imagine an "AI DAO hedge fund" where a pool of agents collectively manage investments, with all actions governed on-chain and profits shared via tokens. This is no longer science fiction – the components (smart accounts, agent frameworks, on-chain data access) are here. The challenge ahead lies in refining these frameworks for scalability and safety, and educating developers on how to use them effectively.

Model Context Protocols (MCPs) for Crypto Applications

One of the most important developments connecting AI and blockchain in recent years is the Model Context Protocol (MCP). Introduced as an open standard in late 2024, MCP is essentially a universal interface that lets AI models (like LLMs) connect to external data sources and services securely. It has been likened to a "USB-C for AI applications" – instead of custom-building a new plugin for every exchange, blockchain, or API an AI agent needs, developers can use MCP servers that standardize these connections. In practice, an MCP server is a small service (could be local or remote) that exposes certain tools or data, and an AI agent (the MCP client) can query it in a structured way. This has huge relevance for crypto, because MCPs bridge the gap between AI's need for real-time info and blockchain's siloed nature.

By design, blockchains do not expose rich data to smart contracts easily (the oracle problem), and AI models on their own have no knowledge of live blockchain state or the ability to interact with protocols. MCP solves this by providing standard endpoints for common blockchain tasks. For example:

Security and Control: Because MCP servers can trigger powerful actions (like moving funds), security is paramount. MCP is designed with a capabilities model: an AI agent will only have access to the MCP servers you explicitly give it. So, if you want a read-only analyst agent, you might provide it an MCP server for market data but not one for sending transactions. Conversely, an autonomous trader agent would be given both read and write capabilities but possibly with limits set at the MCP server level (for example, an MCP server could be configured to only allow trades below a certain size, or require confirmations for large moves). The open standard nature of MCP means anyone can run their own server – e.g. you might run an EVM MCP pointing to your private RPC with your wallet, behind your firewall.

Agent Coordination via MCP and Blockchain: MCP itself is about linking AI to tools, but it can indirectly aid multi-agent coordination. For instance, agents can use blockchain-based MCP tools to communicate through contracts. One agent could write a message or data to a smart contract (using an MCP action to call a function), and another agent could read that (via an MCP read). This is a bit clunky compared to direct P2P, but it offers an immutable, timestamped coordination channel. More elegantly, frameworks like Autonolas already coordinate agents via on-chain events (agents watching for specific contract events as triggers). MCP simply standardizes the access. A concrete scenario: imagine a decentralized workforce of AI agents solving tasks on a bounty system. An on-chain contract holds bounties and records task statuses. Agents use an MCP tool to query open tasks, work on them, then call a contract function to submit results. Another MCP tool might let them call an IPFS or Arweave service to fetch extra data or store results. All agents run trustlessly, with blockchain ensuring fair reward distribution.

Recap of MCP Benefits: In summary, Model Context Protocols give AI agents the eyes, hands, and ears to operate in the crypto world:

The creation of a standard like MCP also fosters a community-driven library of integrations. Already we have dozens of open MCP servers with various capabilities, and that list is expanding (covering everything from databases to email to gaming engines, alongside crypto services). This means an agent developer can pick and choose capabilities almost like plugging in LEGO blocks, rather than reinventing the wheel.

Table 4: Selected MCP Servers for Crypto and Blockchain

MCP ServerPurposeFunctions ProvidedExample Usage
EVM MCP Server for blockchain accessEthereum & EVM chain accessRead balances, token holdings, NFT metadata; call smart contract functions; send transactions; ENS name resolution.Agent queries wallet token balance and sends a transfer on Ethereum.
Solana AgentKit MCP for Solana operationsSolana blockchain accessCreate Solana wallet accounts; transfer SOL/SPL tokens; DEX swap instructions; deploy new token mint; monitor network status.Agent swaps USDC for EURC on Solana when FX rate threshold is met.
CCXT Market Data MCP for real-time data across exchangesCentralized exchange dataFetch real-time prices, order book spreads; historical OHLCV data; top trading pairs and volumes across Binance, Coinbase, Kraken, KuCoin, etc.Agent pulls BTC price from multiple exchanges to arbitrage differences.
Exchange Trade MCP (custom via CCXT)Centralized trading execution(With API keys) Place buy/sell orders; check open orders; cancel orders; get account balances on exchanges.Agent dynamically rebalances a portfolio by trading on Binance and Coinbase.
DeFi Actions MCP ("GOAT") with extensive capabilitiesCross-chain DeFi operations~200 on-chain DeFi actions: DEX swaps, provide/withdraw liquidity, lend/borrow assets, stake tokens, claim rewards, bridge assets.Agent automates yield farming: moves stablecoins from Pool A to Pool B for higher APY, then stakes reward tokens.
Thirdweb MCP for multi-chain deploymentMulti-chain contract integrationDeploy new contracts (using templates); call contract write functions; query contract state across 2k+ blockchains via Thirdweb.Agent automatically deploys a standard ERC-20 contract on Polygon and verifies it.
Block Explorer MCPBlockchain data queriesSearch transactions by hash, get transaction details, check address history, read event logs (if supported by an API).Agent scans recent transactions of a target address to flag any risky interactions.
ENS Lookup MCPName service resolutionResolve .eth names to addresses and vice versa, lookup ENS text records.Agent sees instruction "send to bob.eth" and uses MCP to get Bob's address before sending funds.
AI Oracle MCP (Oraichain)AI output verificationSubmit AI model outputs for validation by oracle (test against data or thresholds); receive pass/fail or confidence score.Agent's price prediction is checked by Oracle; only if confidence >90% does agent execute the on-chain trade.

As MCP adoption grows, we expect more EVM-specific MCPs to appear (e.g., ones tailored to certain L2s or with built-in caches for faster responses), as well as domain-specific MCPs for trading (like a portfolio management MCP that wraps a set of trading and lending tools under one interface). The open nature of MCP means even proprietary exchanges or protocols might provide official MCP endpoints to encourage AI agents to use their services.

In conclusion, the Model Context Protocol is a game-changer for AI-agent interoperability. It allows the previously isolated AI agents to plug into the live crypto ecosystem in a standardized, secure way following established protocols. This not only makes individual agents more powerful (by broadening the range of tasks they can do and information they can access), but it also makes it easier to coordinate multiple agents (since they can share common data sources and actions through MCP). With companies like Anthropic, Block, and others driving MCP forward based on open standards, there is momentum towards MCP becoming a universal standard. For the crypto space, this means future AI agents – whether trading bots, DAO treasurers, or development assistants – will be built faster and with greater capabilities, accelerating innovation at the intersection of AI and blockchain.

Conclusion

From intelligent trading bots that adapt to market chaos, to code assistants that harden smart contracts, to frameworks enabling autonomous agents to operate under on-chain governance, the crypto/blockchain space in 2024–2025 is witnessing a Cambrian explosion of AI agent technology. These advancements are deeply technical: they involve integrating real-time data feeds, ensuring secure transaction execution, and crafting protocols for agents to work together and be held accountable on decentralized ledgers. The convergence of AI and blockchain is giving birth to a new class of applications that are autonomous, trust-minimized, and continuously learning.

Crucially, the focus has shifted from mere novelty to practical application and safety. AI agents are now actively managing real assets and systems – hence the emphasis on architectures (like Valory's) that keep them within safe bounds with clear constraints and standards (like MCP) that ensure secure connections to critical resources. The interplay of on-chain and off-chain components is central: off-chain AI provides the brains, while on-chain smart contracts provide the integrity and memory.

Moving forward, we expect to see more hybrid human-AI DAOs, formal verification integrated with AI (to prove agent decisions adhere to rules), and perhaps regulatory frameworks for autonomous financial agents. But as of 2025, the foundation is well laid. AI agents in crypto are trading, building, and governing – in ways that complement human efforts and take on tasks at superhuman speed and scale. By harnessing these agents and the frameworks that support them, the blockchain industry is pushing into a new era where decentralized networks are not just run by passive code, but by active, intelligent agents that can learn and adapt over time.

All the pieces explored – trading agents with deep data feeds, developer agents improving code security, multi-agent frameworks for decentralization, and MCP interfaces connecting it all – combine into a powerful vision of the future: autonomous agents as first-class citizens in blockchain ecosystems. This deep integration of AI is poised to make decentralized applications more efficient, responsive, and safer, ultimately accelerating the adoption of blockchain technology across finance and beyond.

Sources:

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  2. CryptoSlate – Coinbase conducts its first AI-to-AI crypto transaction (Aug 2024) (Coinbase conducts its first AI-to-AI crypto transaction)

  3. Hackernoon – Building an AI Trading Agent Using Anthropic's MCP (Part II) (Bruce Li, 2025) (Building an AI Trading Agent Using Anthropic's MCP - Part II | HackerNoon) (Building an AI Trading Agent Using Anthropic's MCP - Part II | HackerNoon)

  4. ChainGPT Documentation – AI Smart-Contract Auditor (2024) (AI Smart-Contract Auditor | ChainGPT Documentation) (AI Smart-Contract Auditor | ChainGPT Documentation)

  5. arXiv (Xia et al., 2024) – AuditGPT: Auditing Smart Contracts with ChatGPT (AuditGPT: Auditing Smart Contracts with ChatGPT)

  6. Trail of Bits – Evaluating Solidity support in AI coding assistants (Nov 2024) (Evaluating Solidity support in AI coding assistants - The Trail of Bits Blog) (Evaluating Solidity support in AI coding assistants - The Trail of Bits Blog)

  7. The AI Optimist – The Hidden AI Agent Rule Book: Smart contracts save AI? (Interview with Valory CEO, 2023) (The Hidden AI Agent Rule Book: Smart contracts save AI?) (The Hidden AI Agent Rule Book: Smart contracts save AI?)

  8. Binance Square (Jinse Finance) – 2024: The Upgrade and Challenge of Blockchain + AI Agents (2024: The Upgrade and Challenge of Blockchain + AI Agents | 金色财经 on Binance Square) (2024: The Upgrade and Challenge of Blockchain + AI Agents | 金色财经 on Binance Square)

  9. Rhinestone blog – Account Abstraction 2024... (Kurt Larsen, 2025) (Account Abstraction 2024. 2024 set the foundations. 2025 will…) (Account Abstraction 2024. 2024 set the foundations. 2025 will…)

  10. Lithium Digital (Medium) – Agents.Land: Fusion of AI and Blockchain for Autonomous Agents (Mar 2025) (Agents.Land: Pioneering the Fusion of AI and Blockchain for Autonomous Digital Agents) (Agents.Land: Pioneering the Fusion of AI and Blockchain for Autonomous Digital Agents)

  11. Anthropic – Introducing the Model Context Protocol (Nov 2024)(Introducing the Model Context Protocol)

12 BuilderChain – Understanding MCP in BuilderChain Operations (2024) (MCP · Community Portal) (MCP · Community Portal)

  1. Model Context Protocol GitHub – MCP Servers Repository (ongoing, 2024) (GitHub - modelcontextprotocol/servers: Model Context Protocol Servers) (GitHub - modelcontextprotocol/servers: Model Context Protocol Servers)

  2. Glama.ai – Cryptocurrency Market Data MCP Server (Nayshins, 2024) (Cryptocurrency Market Data MCP Server | Glama) (Cryptocurrency Market Data MCP Server | Glama)