Author: Onkar Singh, CoinTelegraph; Compiler: Tao Zhu, Golden Finance
1. Agentic AI Detailed Explanation
Agentic AI refers to a type of artificial intelligence that is designed to act on its own behalf, with a certain degree of independence and decision-making ability.
This type of artificial intelligence does not just process data or respond to commands; instead, it can set goals and make decisions to achieve those goals, usually in a way that mimics humans.
It's like giving artificial intelligence a sense of purpose and the ability to achieve that goal with minimal human intervention.
This distinguishes it from traditional artificial intelligence, which usually requires human input or predefined rules to operate. Agentic AI is self-guided, which means it can make decisions in real time based on the environment and goals.
2. How Agentic AI Works
Agentic AI works by combining advanced machine learning techniques, decision-making algorithms, and continuous feedback loops.
Think of it as a robot that learns from experience and then uses that knowledge to influence its future actions. It generally works in the following way:
Goal Setting: Agentic AI determines its goals based on initial programming or ongoing environmental input. It might be set to complete a specific task, such as optimizing a supply chain or increasing user engagement.
Decision Making: It then analyzes the data and uses algorithms to decide the best course of action to achieve its goals.
Learning and Adaptation: Like all AI systems, Agentic AI learns from both successes and failures. It continuously adjusts its strategy and refines its decision-making process.

The key here is that AI is able to set its own course of action based on real-time input, making it more autonomous than traditional AI systems. So why are people so excited about agent AI?
Three, Advantages of Agent AI
Agentic AI improves efficiency, minimizes human error, and scales seamlessly, making it an ideal choice for industries that require continuous optimization.
Improved Efficiency:Because Agentic AI does not require constant human supervision, it can run 24/7, constantly learning and adapting to new data.
Reduce Human Error:Because Agentic AI makes decisions based on data and algorithms, it is less prone to bias or errors that may be caused by human judgment.
Scalability:Agentic AI can handle large amounts of data and complex tasks across industries while scaling its decision-making processes to meet growing operational needs.
These advantages make Agentic AI particularly attractive in industries such as logistics, healthcare, finance, and customer service, where continuous optimization is key to maintaining a competitive advantage.
IV. Agentic AI Applications
Agentic AI is transforming healthcare, supply chain, finance, and customer service through autonomous, goal-driven decision-making.
Healthcare:In medical research, Agentic AI can autonomously analyze patient data, recommend treatments, and even provide new avenues for drug discovery.
Supply Chain Optimization:AI systems that can set goals, optimize routes, and make inventory management decisions without direct human intervention are already improving the efficiency of global supply chains.
Finance:Agentic AI is used in algorithmic trading, where it can set financial goals and make real-time decisions based on market data to achieve them.
Customer Service:Chatbots and virtual assistants powered by Agentic AI can not only answer questions, but also make decisions that solve customer problems or personalize the customer experience without waiting for input.
Crypto-Specific Examples of Agentic AI Applications
Cryptocurrency Trading and DeFi:Agentic AI can autonomously analyze market trends, adjust trading strategies, and optimize yield farming without human intervention.
Fraud Detection and Compliance:Agentic AI can track illegal transactions, flag potential money laundering activities, and enforce regulatory compliance on-chain.
Smart Contract Security:It can detect vulnerabilities, audit smart contracts, and prevent exploits by identifying suspicious transactions in real time.
NFT and Metaverse Asset Management:Non-Fungible Tokens (NFTs) and virtual assets require valuation, curation, and liquidity management. Agentic AI can evaluate market trends, predict asset appreciation, and recommend the best buy or sell strategies for digital assets.
V. Agent AI vs. Autonomous AI
Agent AI sets and adjusts its own goals, while autonomous AI operates within predefined parameters. The two terms are often used interchangeably, but there is a clear difference between them.
Autonomous AI refers to AI systems that can operate without human intervention, but it usually operates within an established framework or goals defined by humans. It's like a self-driving car, but the parameters it follows are determined in advance.
Agent AI, on the other hand, not only operates autonomously, but can also set and redefine its own goals as it learns from the environment. Autonomous AI is like a car that follows a GPS, while agent AI is a car that can decide the best route and adjust its destination based on real-time traffic data or other factors.
Here is a summary of the differences between agent-based AI and autonomous AI:

Six, agent-based AI, AI agent, generative AI: Which one is more powerful?
Generative AI creates content based on input; AI agents perform tasks based on commands; agent-based AI sets its own goals, makes decisions, and adapts to real-world feedback.
Generative AI is all about creation—whether it’s text, images, music, or video. It doesn’t make decisions or set goals; it just generates content based on the input it receives.
For example, ChatGPT is a generative AI. If you ask it to write an essay, a poem, or code, it will generate it, but it won’t decide on its own what needs to be written.
In contrast, AI agents are designed to complete specific tasks on command. Unlike generative AI, which produces creative output, AI agents focus on action — retrieving information, automating processes, and executing user requests.
Siri and Alexa are examples of AI agents. If you ask Alexa to set an alarm, play a song, or turn off the lights, it will do it efficiently. However, it won’t decide on its own when the alarm needs to go off or which playlist will suit your mood unless you explicitly program it.
Agent AI goes a step further. Not only can it create content like generative AI or follow commands like an AI agent, it can also set its own goals, make decisions, and adjust based on real-world feedback.
For example, imagine an AI-powered investment robot. A traditional AI agent can buy cryptocurrencies when you tell it to, but agent AI can analyze the market, develop an investment strategy, and adjust its approach without human intervention. It thinks ahead, learns from past performance, and refines its strategy over time.
Think of agent AI as a self-driven entrepreneur who not only processes tasks but also decides which tasks are worth pursuing and how to improve them. This is the next level of AI autonomy.
Here is a summary of the differences between generative AI, AI agents, and agent AI in terms of purpose and autonomy:

But which one is more powerful?
It depends on the use case. If you need content, generative AI may be your best choice. If you need task execution, AI agents are reliable.
But if you need an AI that can think, plan, and adapt, Agent AI might be your best choice
The real breakthrough will be combining all three into one system - an AI that can generate, execute, and optimize decisions on its own. It looks like this is where AI is going!