AI agent decoding: shaping the intelligent force of the future new economic ecosystem

AI agents are revolutionizing the cryptocurrency economy, showing great potential from Decentralized Finance to GameFi. This article is from an article written by Klein Labs and compiled by PANews. (Synopsis: Y Combinator Entrepreneurship Guide Interpretation: What are the future trends of AI agents? (Background added: Bankless: How encryption technology is becoming a superfuel for AI agents) 1.1 Introduction: "New Partners" in the Intelligent Age Each cryptocurrency cycle brings with it a whole new infrastructure that drives the entire industry. In 2017, the rise of smart contracts spawned a boom in ICOs. In 2020, DEX's flow pool brought the summer boom of Decentralized Finance. In 2021, a large number of non-fungible token series works were launched, marking the beginning of the era of digital collectibles. In 2024, pump.fun's outstanding performance led to the memecoin and launch platform boom. It should be emphasized that the start of these verticals is not only due to technological innovation, but also the result of the perfect combination of financing models and bull market cycles. When opportunity meets the right moment, it can lead to great change. Looking ahead to 2025, it's clear that the emerging area of the 2025 cycle will be AI agents. This trend peaked last October, with the launch of $GOAT Token on October 11, 2024, and reaching $150 million Market Cap on October 15. Then, on October 16, Virtuals Protocol launched Luna, which debuted as the IP live broadcast image of the girl next door, which exploded the industry. So, what exactly is an AI agent? Everyone must be familiar with the classic movie "Resident Evil", in which the AI system Queen of Hearts is impressive. Queen of Hearts is a powerful AI system that controls complex facilities and security systems, autonomously sensing the environment, analyzing data, and acting quickly. In fact, the AI Agent shares many similarities with the core functionality of Queen of Hearts. Real-world AI agents play a similar role to some extent, acting as "smart guardians" of modern technology, helping businesses and individuals cope with complex tasks through autonomous perception, analysis, and execution. From self-driving cars to intelligent customer service, AI agents have penetrated into every industry and become a key force for efficiency and innovation. These autonomous agents, like invisible team members, have a full range of capabilities from environmental perception to decision-making execution, gradually penetrating into various industries, promoting the dual improvement of efficiency and innovation. For example, an AI AGENT can be used to automate trading, managing portfolios and executing trades in real time based on data collected from Dexscreener or social platform X, constantly optimizing its performance in iterations. AI AGENTS are not monolithic, but are divided into different categories based on specific needs in the encryption ecosystem: Executable AI Agents: Focus on completing specific tasks, such as trading, portfolio management, or Arbitrage, with the aim of improving operational accuracy and reducing the time required. Creative AI Agent: Used for content generation, including text, design, and even music creation. Social AI Agent: Act as an influencer on social media to engage with users, build communities, and engage in marketing campaigns. Coordinated AI Agent: Coordinates complex interactions between systems or participants, especially for multi-chain integration. In this report, we will delve into the origins, current status, and broad application prospects of AI agents, analyze how they are reshaping the industry landscape, and look ahead to their future trends. 1.1.1 History THE DEVELOPMENT HISTORY OF AI AGENTS SHOWS THE EVOLUTION OF AI FROM BASIC RESEARCH TO WIDESPREAD APPLICATION. At the Dartmouth Conference in 1956, the term "AI" was first proposed, laying the foundation for AI as a stand-alone field. During this period, AI research focused primarily on symbolic methods, giving rise to the first AI programs such as ELIZA (a chatbot) and Dendral (expert systems in the field of organic chemistry). This phase also witnessed the first proposal of neural networks and the initial exploration of machine learning concepts. But AI research in this period was severely constrained by the limitations of computing power at the time. Researchers have encountered great difficulties in natural language processing and the development of algorithms that mimic human cognitive functions. In addition, in 1972, mathematician James Lighthill submitted a report on the state of ongoing AI research in the UK, published in 1973. The Lighthill report essentially expressed a general pessimism about AI research after an early period of excitement, triggering a huge loss of confidence in AI from ( UK academic institutions, including funding agencies ). After 1973, AI research funding decreased significantly, and the field of AI experienced its first "AI winter", and skepticism about the potential of AI increased. In the 80s of the 20th century, the development and commercialization of expert systems led to the adoption of AI technology by enterprises around the world. This period saw significant advances in machine learning, neural networks, and natural language processing, driving the emergence of more complex AI applications. The introduction of the first autonomous vehicles and the deployment of AI in various industries such as finance and healthcare also marks an expanded suite of AI technologies. But in the late 80s and early 90s of the 20th century, the AI field experienced a second "AI winter" as market demand for dedicated AI hardware collapsed. In addition, scaling AI systems and successfully integrating them into real-world applications remains an ongoing challenge. But at the same time, in 1997, IBM's Deep Blue computer defeated world chess champion Gary Kasparov, a milestone in AI's ability to solve complex problems. The revival of neural networks and Deep learning laid the groundwork for the development of AI in the late 1990s, making AI an integral part of the technological landscape and beginning to impact everyday life. By the turn of the century, advances in computing power had fueled the rise of Deep learning, with virtual assistants like Siri demonstrating the usefulness of AI for consumer applications. The 2010s saw further breakthroughs in generative models such as reinforcement learning agents and GPT-2, pushing conversational AI to new heights. In this process, the emergence of Large Language Model (LLM) has become an important milestone in the development of AI, especially the release of GPT-4, which is regarded as a turning point in the field of AI agents. Since the release of the GPT series by OpenAI, large-scale pre-trained models have demonstrated language generation and comprehension capabilities that surpass traditional models through tens of billions or even 100s of billions. Their excellence in natural language processing enables AI agents to demonstrate logical, coherent interactions through language generation. This enables AI agents to be applied to scenarios such as chat assistants, virtual agents, and gradually expand the suite to more complex tasks such as business analysis, creative writing. The learning ability of large language models provides AI agents with ...