AI agents: how do digital minions work in the crypto market?

AI agents are rapidly changing the approach to cryptocurrency trading, market analysis, and risk management. They don’t just perform algorithmic tasks according to predefined criteria like traditional bots, but adapt to conditions, learn from previous experience, and provide a new level of autonomy in interaction with the blockchain. Why are these «minions» crypto markets so interesting?

Artificial intelligence (AI) is being actively integrated into various fields, and the cryptocurrency market is no exception. Autonomous programs, such as AI agents, are able to perform tasks without constant human control, analyzing large amounts of data and making decisions based on it. Analysts already predict that the number of such projects will grow in the coming years, and their impact on the market will increase.

AI agents vs. trading bots

The functionality of AI agents and trading bots is partially similar, but these tools have many differences:

• AI agents use artificial intelligence to analyze large amounts of data, adapt to new market conditions, and learn from previous experience. They are able to detect complex patterns, predict trends, and make autonomous decisions. In addition to trading, AI agents can perform other tasks such as analyzing social media to identify trends, managing risk, making predictions based on blockchain metrics, and interacting with other platforms or users. They can also use unstructured data, such as news, to track social media sentiment and general blockchain activity, and then independently draw conclusions about the relationships between events and influence strategy. AI agents have a wide range of integration and user interaction capabilities.
• Trading bots work according to pre-programmed rules and algorithms. They execute trades based on predefined conditions (e.g., price, volume, time), but they cannot learn or change their strategy on their own. They only work with structured data, and their functionality is limited to trading and transaction execution. Trading bots mostly work in the background without active user interaction, usually integrate only with specific exchanges, and have limited functionality.

Therefore, AI agents in cryptocurrencies are more versatile and adaptive tools that can be considered the next stage of evolution of trading bots. AI agents can perform a wide range of tasks, while trading bots are focused on executing specific trading strategies according to predefined parameters.

How AI agents work

They usually have a structure consisting of three levels:

1. Level of data collection and input. At this stage, the agent collects information and necessary data for further processing. It connects to blockchain nodes (nodes) or APIs such as Web3.js or ethers.js to receive real-time and historical data (transactions, smart contract states, etc.). Then there areIntegration with oracles such as Chainlink to access data outside the blockchain, or APIs to collect market information, analyze social media sentiment, etc.
2. Level of AI/machine learning (AI/ML). This is where analytics takes place: decision-making or forecasting. The agent inhe use of certain models for financial forecasts, such as LSTM (Long Short-Term Memory) networks, Random Forests, or reinforcement learning methods for more complex strategies The AI model is trained on historical data, and then can make decisions in real time.
3. Level of interaction with the blockchain. At the final stage, the AI agent interacts with smart contracts compatible with, for example, the Ethereum Virtual Machine (EVM) via the ABI (Application Binary Interface). Further inlibraries are used to sign transactions, estimate gas (the cost of paying to execute a transaction), and manage noncesA unique number assigned to each account transaction to ensure order and avoid duplicate processing. to ensure that transactions are executed correctly. This allows agents to perform actions on behalf of the user directly on the blockchain.

How AI agents are used in cryptocurrencies

1. Automated trading. AI agents analyze market trends in real time, which allows them to execute trades with maximum efficiency. They can detect patterns that humans would not notice and react to them instantly.
2. Market metrics analysis: AI agents use blockchain metrics such as hashrate, mining complexity, and transaction value to predict price movements. Studies show that these indicators can be useful for operations in the cryptocurrency market
3. Risk management. By analyzing historical data and current market conditions, AI agents can assess potential risks and recommend strategies to mitigate them.
4. Fraud detection and security: analysis of smart contracts, identity verification and KYC processes, inIdentify anomalies in transactions
5. Optimization of electricity consumption, portfolio, and much more.

According to the market data as of January 8, the total capitalization of the AI agent sector in cryptocurrencies is almost $13 billion. This list includes 93 tokens. The current market capitalization of 546 DeFi tokens is $147 billion.

Popular AI agents

The growing popularity of AI agents in cryptocurrencies has led to the emergence of innovative projects, each with unique applications and features.

1. Goat (GOAT) and Truth Terminal

Goatseus Maximus (GOAT) memecoin has gained popularity due to Truth Terminal – An AI agent created to generate viral hype around the token. The campaign turned GOAT into a cultural phenomenon, increasing its market capitalization to more than $415 million in just two weeks after its launch.

2. Zerebro (ZEREBRO)

Inspired by neural networks, Zerebro focuses on creating AI solutions for portfolio management. With a current market capitalization of $362 million, Zerebro offers tools for blockchain analysis and risk assessment.

3. AIXBT by Virtuals (AIXBT)

AIXBT specializes in blockchain analytics and trade automation. With a market capitalization of $362 million, AIXBT’s appeal lies in its ability to process large amounts of data and identify profitable opportunities in real time. “AIXBT is at the forefront of this cryptocurrency revolution. This AI agent provides intelligent data by analyzing information from over 400 key opinion leaders (KOLs) on social media and provides actionable insights, risk assessments, and technical analysis.

4. Luna by Virtuals (LUNA)

Luna by Virtuals (LUNA) combines generative AI with NFT to offer a platform for creating intelligent digital art. Despite recent market fluctuations, LUNA has maintained a market capitalization of $80 million, which demonstrates the high demand for AI solutions in the creative industry.

5. Virtuals Protocol (VIRTUAL)

Virtuals Protocol — is a decentralized platform on the Base blockchain that allows the creation and co-ownership of AI agents, focusing on applications in the gaming and entertainment industry. Users can develop virtual influencers and interactive NFTs that autonomously interact with audiences across different platforms. These agents are tokenized, which allows investors to invest and participate in the development and management of their activities. The protocol uses a token redemption and burning mechanism to share the revenue generated by AI agents with token holders. A portion of the revenue earned by agents through interactions and services is used to buy back tokens from the open market, after which they are burned. This reduces the supply of tokens, potentially increasing the value of the remaining tokens, which creates financial benefits for investors.

Risks of using cryptocurrency AI agents

AI agents have great potential and will be a trend in 2025, but they may face many problems:

1. risk of hallucinations. AI agents can misinterpret data, which leads to wrong decisions. For example, a trading agent may mistakenly predict a market trend and execute unprofitable trades.
2. Scalability issues. Most blockchains are not optimized for the real-time interactions required by AI agents. For example, an agent that manages liquidity through different DeFi protocols may face delays or high gas fees during network congestion, which will significantly reduce its performance.
3. Imperfection of technology. Early adoption may put users at risk due to bugs, limited features, and uncertainty in the regulatory environment. For example, AI agents that promote tokens or execute trading operations without clear rules may fall into legal gray areas.