If you have been following the cryptocurrency market since late 2024, you may have come across a leading narrative centered around AI agents. This trend has drawn significant attention from investors, particularly toward coins dabbed with AI agents, including meme coins.
Cryptocurrency aggregators like CoinMarketCap and CoinGecko have even created dedicated categories labeled “AI Agent Coins,” placing emphasis on the growing interest among various players in the space. Given the sudden influence of this development on the cryptocurrency market, it is worth understanding its nature—and that is precisely what I aim to explore in this article.
What is Artificial Intelligence (AI)
Artificial Intelligence (AI) can be defined as the process of enabling machines to mimic human behavior through computer programs. It primarily focuses on tasks and activities that require human intelligence.
AI systems perform more effectively when they have access to large volumes of data. This enables them to identify patterns and relationships that may not be immediately apparent to humans. Just as humans develop learning strategies, AI relies on algorithms to analyze data and make decisions. From a technical standpoint, the functionality and efficiency of an AI system depend on stout infrastructure, including cloud services, decentralized networks, and APIs. These components ensure seamless data processing, storage, and interaction with other systems.
Over the years, AI has played a significant role across numerous industries. Businesses, from large corporations to small enterprises, have actively explored how AI can enhance their operations. This pursuit is widely seen as a way to gain a competitive advantage and streamline business processes.
AI agents
In philosophy, an agent is often described as an entity capable of acting, making decisions, and initiating actions that are intentional, autonomous, and guided by a clear rationale or purpose. As previously explained, with an understanding of AI and the concept of an agent, you may begin to grasp the idea of an AI agent.
In the cryptocurrency space, an AI agent can be defined as a computer program designed to perform specific tasks related to blockchains and cryptocurrencies. Thanks to machine learning, these programs learn and improve over time through repeated iterations of their tasks. Eventually, they can make optimal decisions independently without requiring human intervention.
How do AI agents work
The sense-think-act model can help us better understand how AI agents work. According to this framework, an AI agent begins by gathering raw data from its environment (Sense), processes this data to identify patterns and make informed decisions (Think), and then takes appropriate actions based on those decisions (Act).
The raw data used by AI agents can come from various sources, including on-chain transactions from the blockchain and smart contracts. Market data—such as trading volume and order book information—can be gathered from both centralized and decentralized exchanges. Additionally, data may be sourced from off-chain inputs via oracles or directly from user interactions.
Once the data is retrieved, various techniques can be applied to process it. AI agents may use natural language processing (NLP), machine learning (ML) models, reinforcement learning, and technical indicators embedded in algorithms, among other methods. Based on the agent’s analysis and objectives, it takes strategic action—for example, making decisions related to balancing a crypto portfolio.
The process does not end once a task is completed. Unlike regular bots, AI agents continuously improve over time. They refine their performance through feedback loops, predictive modeling, and adaptive strategies, allowing them to optimize decision-making dynamically.
Example in Practice
For instance, hundreds of thousands of meme coins are launched every week. With approximately 95% of these coins failing, identifying the next “moonshot” becomes an almost impossible task. To address this challenge, we could develop an AI agent that scans crypto analytical platforms for newly launched coins, following a predefined strategy. Once certain conditions are met, the AI agent can execute a buy order. Over time, it would refine its strategy by analyzing the performance of previously selected coins, improving its ability to make nearly exact decisions at launch.
How AI agents are used in Crypto
Think of any task or activity you frequently perform in the cryptocurrency space. Now imagine developing an AI agent to automate that process, solving problems or completing tasks without your direct involvement. Here are some examples of how AI agents could be employed:
- Trading: AI agents can execute cryptocurrency and NFT trades using advanced strategies, optimizing both timing and profitability. With algorithmic trading, specialized AI systems can process and execute large volumes of transactions far beyond human capacity. This provides traders with a competitive edge, especially given the volatile nature of cryptocurrency prices. AI agents utilize algorithms to autonomously execute trades based on various strategies, including range trading, swing trading, arbitrage, and other complex techniques. These strategies often rely on technical indicators such as Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), and Fibonacci retracement, among others.
- Sentiment Analysis: Sentiment analysis involves examining online text to determine the underlying emotions being conveyed—insights that may not be immediately obvious. AI agents can scan online platforms, such as social media, for unstructured data and process it using NLP technologies. During the processing stage, keywords are identified by first breaking sentences into components, and then reducing words to their root forms. Next, stop words (e.g., “a,” “with,” “is,” “the”) are removed. Once preprocessing is complete, NLP technology analyzes the extracted keywords and assigns a sentiment score. The insights gained from sentiment analysis can serve as valuable indicators for investors, helping them gauge market sentiment and make informed decisions.
- Meme Coin Hunting: AI agents can streamline the process of identifying meme coins that meet specific criteria and report their findings via instant messaging apps like Telegram. Given the sheer number of meme coins launched daily, manually tracking the market becomes nearly impossible. However, AI agents can automate the trading process, making it more efficient and data-driven. Modern trading platforms offer advanced features that assist in investment decision-making. AI agents can be designed to leverage these functions, continuously optimizing their decisions over time for better trading outcomes.
- Smart Contract Analysis: Smart contracts are self-executing computer programs stored on a blockchain that run automatically when predefined conditions are met. They play a crucial role in DeFi applications but are also vulnerable to attacks. Traditionally, humans manually review these contracts to identify errors and security flaws. AI agents, however, can be leveraged to audit smart contracts, detecting potential vulnerabilities more efficiently. By automating this process, AI can help reduce the risks associated with hacks and exploits, enhancing the overall security of blockchain applications.
- DeFi Optimization: AI agents can identify decentralized finance (DeFi) protocols and liquidity pools (LPs) that offer the highest returns. This strategy, traditionally known as yield farming, is executed on decentralized exchanges known as automated market makers (AMMs).In practice, users must supply equal-value amounts of a trading pair (e.g., BTC/USDC) to an LP. In return, they earn a share of the total trading fees generated by the pool, proportional to their contribution. Individual allocations are tracked using LP tokens, which can also be utilized for additional yield-generating activities. Maximizing rewards in yield farming can become increasingly complex. In such cases, AI agents can automate the process, optimizing decisions without requiring manual input from the trader—other than supplying assets.
Final thoughts
Throughout this article, we have explored AI agents and their various applications, including automated trading, portfolio management, and on-chain data analysis to support decision-making. Currently, some projects provide infrastructure for AI agent development, allowing individuals and organizations to create specialized AI agents. These agents can handle a wide range of tasks, extending their utility beyond cryptocurrency and blockchain. Additionally, other projects offer AI-powered services and accept payments using their native tokens, highlighting the growing significance of AI in the industry.
