Why Crypto AI Agents Will Change Everything
AI Agents are at a stage that — with a little help — change everything we do in crypto.
The emergence of AI agents marks a significant milestone in the evolution of artificial intelligence, unlocking possibilities for autonomous AI systems and novel applications across various domains. This article focuses on how crypto will use AI agents by defining AI agents, highlighting their importance, distinguishing them from conversational AI and chatbots, trading bots, and setting the stage for a deeper exploration of agent behavior.
First, What Is An AI Agent?
AI agents, unlike existing programs, are autonomous, meaning they can make decisions and take actions without human intervention based on their programming, learning, and data processing. The agents are a key feature in the emerging AI landscape and arguably will have a greater impact on crypto that LLMs or GPUs.
Different From Conversational AI (ChatGPT)
AI agents differ from conversational AI (LLMs) or chatbots primarily in their scope and functionality. While conversational AI focuses on simulating human-like dialog within a narrow domain of knowledge, AI agents are designed to perform a broader range of complex, multi-step tasks. This distinction underscores the versatility and potential of AI agents to build on LLMs and impact industry, not only crypto. They enable the automation of complex, dynamic tasks that require real-time analysis, decision-making, and adaptability, which exceeds the capabilities of conversational chatbots. Although intelligent agent-based systems might still have conversational interfaces to set human goals, obtain feedback, and share results, their core functionalities are much broader.
How AI Agents Are Applied To The Crypto Space
AI Agents are most effective when they operate in a specific domain. In our case, that is crypto investing. We will not be looking at image generation, security, or code augmentation. Agents can continuously monitor market conditions, track price movements, and analyze vast amounts of data from multiple sources, including social media, news articles, and historical market trends. By processing this information at high speed, AI agents can identify trading opportunities and provide this insight to users. They can also execute trades with precision, far surpassing human capabilities in terms of speed and accuracy. This autonomous operation enables AI agents to react to market changes instantaneously 24/7, making them ideal for the volatile crypto market.
AI agents’ ability to learn and adapt based on a user's behavior or feedback enhances their effectiveness. When giving an AI Agent a task or goal, it needs to work out all of the steps needed to achieve that goal. They can simulate various scenarios and adjust their approaches to minimize risks and maximize profits. Additionally, AI agents can manage trading portfolios by simultaneously managing trading strategies, ensuring a balance between risk and reward. Crypto operates on a 24-hour basis and AI Agents do as well, minimizing the problems that people have when trying to trade but also needing to sleep and live their lives.
Agentic Behavior
To understand autonomous agents, we need to examine the behaviors and attributes that enable them to perform tasks with a high degree of independence and efficiency. This section delves into the core components that constitute agentic behavior.
Autonomy
Autonomy is the cornerstone of agentic behavior, empowering AI agents to operate independently, make decisions, and act without direct human intervention. This is achieved through goal orientation, allowing agents to self-organize, adapt to their environments, and optimize their actions to achieve specific objectives. Autonomy encompasses the agent’s ability to dynamically adjust its behavior and recalibrate its strategies based on the task at hand and its current state, ensuring effective and efficient performance without the need for external guidance.
When a user gives an agent a task, such as to research new and upcoming AI crypto tokens, the agent needs to decide what steps need to be taken for it to fulfil the goal. If the agent has any ambiguity it can always ask the user to gain clarity.
Goal Orientation
At their core, autonomous agents are designed with a purpose, in our case to invest in crypto. This goal-oriented nature ensures that every plan, decision, and action taken by the agent is directed toward achieving specific, predefined objectives. This focus drives the agent’s actions and guides the development of its capabilities, ensuring impactful results in pursuit of its goals.
Memory
For autonomous agents to effectively navigate their environments and perform tasks, they must possess reliable memory. This involves the ability to sense the environment, store relevant information, recall past experiences, and utilize this knowledge to inform current and future actions. Memory enables agents to plan, track progress, learn from experiences, adapt strategies, and make informed decisions based on historical data.
- Long-term Memory: Enables AI agents to store and retrieve vast amounts of information over extended periods both user information and market data. This memory type is crucial for learning from past experiences, improving decision-making, and adapting to new situations. An example of this would be market data and historic trend/singht data.
- Short-term Memory: Focuses on the temporary storage of information necessary for the immediate execution of tasks. It allows agents to keep track of ongoing processes and make quick, informed decisions. This would include the entire thread of the specific conversation you are having with the AI.
Environment Sensing
AI agents must perceive their surroundings to interact effectively with the world or other agents. Environment sensing involves collecting data or understanding the state of other agents, interpreting this data to understand the current state of the environment, and identifying changes that may influence the agent’s decisions and actions. The environment that is relevant to crypto today are the crypt and financial markets, crypto twitter and related social media as well as political sentiment.
Tools
Tools refer to the specific competencies and functionalities that autonomous agents possess or acquire over time. These tools enable agents to perform a wide range of tasks, from accessing information on the web and executing code to more complex functions like strategic planning and problem-solving. Tools development and refinement are essential for enhancing the agent’s capabilities and effectiveness in fulfilling its objectives. An example of a tool that a AI agent would use in crypto would be a trading bot like Unibot.
Planning
A critical aspect of agentic behavior is the agent’s ability to plan and execute actions toward achieving its goals. This involves strategizing, breaking down complex objectives into manageable tasks, and devising a sequence of steps to accomplish these tasks.
- Chain of Thoughts: Represents the agent’s ability to engage in complex reasoning, considering multiple factors and potential outcomes before making a decision.
- Task Decomposition: Involves breaking down a larger goal into smaller, manageable tasks, enabling a more structured approach to problem-solving.
- Task Sequencing: The process of determining the optimal order in which to execute tasks, considering dependencies, priorities, and resource availability.
Action
An agent must be able to act on its current plan, performing tasks and taking necessary actions to accomplish those tasks. Moreover, the agent must evaluate the outcomes of its actions, reflect on the next steps, and make necessary adjustments to its plan. This cycle of planning, action, and reflection ensures that the agent can successfully navigate challenges and progressively move toward its objectives.
- Task Execution: Refers to the agent’s ability to carry out the tasks it has planned, applying its skills and resources to achieve its objectives.
- Function (Tools) Calling: Involves invoking specific functions or procedures that the agent has at its disposal to perform particular actions or calculations.
- Self-Reflection: Allows an agent to evaluate its actions and outcomes, learning from successes and failures to improve future performance.
Use Cases in Crypto
Now that we have a good overview of what an AI Agent does, and how it works we need to talk about how it’s applied in crypto. There are three key areas in which AI Agents have a huge impact on crypto investing.
Portfolio Management
AI Agents are well suited for protecting a user's crypto portfolio. They can constantly monitor all of the user's positions across all chains and protocols, preventing liquidations, managing funding costs, and watching price action for known behaviors that can lead to loss. A portfolio agent will have the primary task of protecting wealth.
Another major use case is portfolio rebalancing. An AI Agent can have an overview of the markets and an understanding of a user's investment goals to rebalance the portfolio and keep a degree of safety and diversification.
Trading
Trading is the AI Agent use case that people would most commonly be familiar with. When an AI Agent is connected to insights, market data, and a fast trading bot, it can do some pretty amazing things. The trading agent would work in concert with the portfolio and research agent to execute the output of the other two agents.
The biggest opportunity is timing where the AI Agent will be able to monitor the markets and make split-second trades based on your goals. By balancing price, market cap, gas fees, and insights it will find the best path forward.
Research
A research AI Agent can analyze the market for gems and learn from the user what types of investments they prefer. It can learn which narratives are relevant to the user and analyze tokens seconds after launch. By accessing market data, Discord, Telegram, and X (formerly Twitter) insights the research can be both quantitative and qualitative in nature.
A research agent would then provide information and findings to both the portfolio and trading agent to achieve their goals more effectively.
In conclusion, this brief description of AI agents shows we are stepping into a new era of (crypto) technology that is more intelligent and self-sufficient than ever before. This journey holds incredible potential for AI to change how we trade, manage risk, and interact with the market. By combining the autonomy of AI agents with the understanding and creativity of LLMs, we are opening up new possibilities for the smallest investor and the biggest of institutions.
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