AI Agents

An AI agent is an autonomous software program designed to perform complex tasks on a decentralized network with little to no human input.

What Is an AI Agent in Crypto?

An artificial intelligence (AI) agent is a self-operating piece of software capable of communicating with its environment to achieve set objectives. This means that it can observe, learn from experience, and make decisions based on what the task needs. 

That said, you can think of a crypto AI agent as a digital assistant that can perform most of your digital tasks. This includes managing your crypto portfolio, automatically performing transactions, creating generative art, managing voting, learning and improving from experience, and so on. You could even create an intelligent NFT (iNFT) that evolves based on user interactions.

Crypto AI Agents Vs Bots

Like bots, they automate tasks, respond to queries, and help users in completing tasks. However, what separates AI agents from bots comes down to their probabilistic nature. 

To explain this, bots are deterministic, in that they adhere to a predefined set of rules and perform tasks based on instructions. On the other hand, AI agents are probabilistic, where they leverage machine learning (ML) and models to analyze data, speculate outcomes, and make decisions. 

These agents are continuously learning and embody flexible decision-making. This approach enables them to adapt to new trends and patterns, allowing them to perform intelligent actions and complex tasks. Conversely, bots are only capable of performing simple and repetitive tasks since they are pre-programmed to provide a limited set of responses.

How Do AI Agents Work?

The workings of these intelligent agents can be simplified into four steps: observation, processing and decision-making, action, and learning.

• Observation – The agents constantly gather information from their environment, such as real-time market data, user input, blockchain transactions, news, and social media chatter.
• Processing and decision-making – They analyze the collected dataset using complex algorithms and ML models to find patterns and trends. Based on the findings of the evaluation, the agent makes a prediction and decides the best course of action for the task.
• Action – The agent automatically performs the task or executes the decision made, such as placing a trade, generating a digital art, or sending a notification.
• Learning – The agents self-reflect, meaning that they review their actions and learn from the outcome of these actions to improve their future performance, output, and decision-making.