Understanding Agentic AI, Generative AI, and AI Agents

Understanding Agentic AI, Generative AI, and AI Agents

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The concept that most people are familiar with is Generative AI, such as large language models (LLMs) like ChatGPT, Doubao, and Kimi, which can answer questions, generate text, and assist in completing other tasks.
However, this operation is passive, meaning that Generative AI can only respond to received inputs based on learned patterns. LLMs cannot make decisions independently nor plan or adapt to changing situations.
The emergence of Agentic AI (currently translated variously as agent-based AI/intelligent AI/autonomous AI, but we will use the English term for differentiation) addresses this issue.
Unlike Generative AI, Agentic AI can take proactive actions, set goals, and learn from experiences.Agentic AI can adjust its behavior over time and handle complex tasks that require continuous decision-making.
The transition of AI from passive to active opens up new possibilities for technology in many industries.

The Concept of Agentic AI

Agentic AI refers to AI systems that can make decisions autonomously and take actions to achieve specific goals. Unlike merely generating content, these AI models can interact with their environment, respond to changes, and complete tasks with minimal human guidance.
The core of Agentic AI lies in autonomy.This means it can make decisions, take actions, and even learn independently to achieve specific goals. Its existence is akin to having a virtual assistant that can think, reason, and adapt to a constantly changing environment without needing continuous guidance.

Understanding Agentic AI, Generative AI, and AI Agents

As shown in the image above, there are four key stages in the operation of Agentic AI:
  1. Perception: Collecting data from the surrounding environment.
  2. Reasoning: Processing this data to understand what is happening.
  3. Action: Deciding what action to take based on understanding.
  4. Learning: Continuously improving and adapting over time, learning from feedback and experiences.
For example, a virtual assistant with Agentic capabilities can not only provide information but also schedule appointments, manage reminders, or perform other actions to help users achieve their goals.
Autonomous driving solutions are also typical examples of Agentic AI. In driving scenarios, AI can systematically perceive the surrounding environment, make driving decisions in real-time, navigate roads safely, and reach destinations independently. Agentic AI can learn from each trip to better cope with complex traffic rules and unexpected situations.

Comparison Between Generative AI and Agentic AI

As mentioned earlier, Generative AI is a type of artificial intelligence focused on creating new content, such as text, images, music, or videos. It learns to understand patterns, styles, or structures from vast amounts of data, and then generates original content based on what it has learned.
Understanding Agentic AI, Generative AI, and AI Agents
The left side shows how Agentic AI works through an iterative, cyclical workflow that includes “research” and “revision” stages. This process involves continuous self-assessment and improvement, allowing Agentic AI to produce higher quality, optimized outputs. By taking multiple steps to test and improve its work, Agentic AI can operate independently and learn from each stage while handling tasks that require ongoing evaluation and adjustment.
On the right, Generative AI follows a simple one-step workflow: it goes directly from “start” to “finish” in one go. This means that AI provides an immediate response without revisiting or improving its output. This process is linear, producing a basic result that satisfies the initial prompt without considering subsequent iterations or tests. In other words, Generative AI has certain limitations when handling more complex or adaptive tasks.
The characteristics of the two are quite different.

Agentic AI

  • Autonomy: Agentic AI can act independently without continuous human input. It can be imagined as a robot that operates without human control, making decisions about the next steps based on its surroundings and executing them.
  • Goal-Oriented: Agentic AI is directed by clear goals. It does not respond randomly to the world but actively works towards a specific goal. For example, the goal of an autonomous vehicle is to safely take you to your destination, and each of its actions, from turning to braking, serves this goal.
  • Continuous Learning: Agentic AI learns from its actions and experiences. When encountering problems or failures, it adjusts. For instance, an AI recommending movies will learn your preferred genres and improve over time to provide better suggestions.
  • Suitable for Complex Decisions: Agentic AI does not just make simple choices; it evaluates many options and considers the outcomes. For example, an AI controlling stock trading algorithms analyzes vast amounts of data, predicts trends, and decides whether to buy or sell stocks based on this information. In the future, perhaps AI can offer highly personalized services in finance—adjusting financial advice or investment strategies based on real-time data and predictions.
  • Environmental Perception: For AI to make informed choices, it needs to understand its environment. Agentic AI can achieve this through sensors or data. For example, a robot uses cameras to “see” obstacles and then navigates around them.

Generative AI

  • Limited Autonomy: The autonomy of Generative AI is limited. It does not act independently and requires human input to generate responses. It processes received inputs and generates outputs based on learned patterns, but cannot initiate actions or operations without external prompts.Some Generative AI models (like certain types of neural networks) do have a degree of learning ability, but this learning is far less dynamic and autonomous than that of Agentic AI.

  • Task-Oriented: Generative AI is passively task-oriented. It responds to specific prompts or tasks by generating relevant content (such as text or images), but it does not pursue long-term goals or have an overarching aim. Each task is completed based on immediate input.

  • Only Involves Basic Decisions: Generative AI engages in basic decision-making. It selects outputs based on learned patterns and does not evaluate multiple alternatives or consider consequences. For example, when generating text, it selects the most likely next word or phrase based on training but does not make complex, multi-layered decisions.

  • No Learning or Adaptation: Generative AI does not learn or adapt in real-time. Once training is complete, it operates based on the patterns learned during training, but it will not change or improve its performance based on new interactions unless retrained with updated data.

  • No Environmental Perception: Generative AI lacks environmental perception. It can process data such as text or images but cannot perceive or interpret the physical environment. It cannot understand its surroundings and only reacts to given inputs without any external awareness.

Practical Application Comparison

The theoretical distinctions between Generative AI and Agentic AI are evident in real-world scenarios as well. Generative AI may have unique advantages in creative content generation and artistic creation, while the applications of Agentic AI will be broader.
There have been experiments conducted by teams using coding tasks, finding that results under the Agentic workflow were more accurate.
Because under zero-shot prompts (i.e., requiring AI to solve problems directly without additional guidance or breaking them down into smaller steps), the Agentic workflow can decompose tasks into smaller steps, such as understanding the problem, writing code in parts, testing, and fixing errors, allowing AI to iterate and improve over time.
In addition to the previously mentioned applications in autonomous driving, Agentic AI has great potential in the following areas:
  • Supply Chain Management: Helping companies optimize their supply chains. By autonomously managing inventory, predicting demand, and adjusting delivery routes in real-time, AI can ensure smoother and more efficient operations. For example, Amazon’s warehouse robots, powered by AI, can navigate complex environments, adapt to different conditions, and autonomously move goods within the warehouse.
  • Cybersecurity: In the field of cybersecurity, Agentic AI can detect threats and vulnerabilities by analyzing network activities and automatically responding to potential intrusions.
  • Healthcare: Assisting in diagnosis, providing treatment suggestions, and managing patient care. Agentic AI can analyze medical data and help doctors make more informed decisions. For example, IBM’s Watson Health uses AI to analyze vast amounts of medical data, learning from new information to provide insights that assist doctors and healthcare professionals.

What is an AI Agent?

AI Agents are typically created for specific tasks. They are often designed as task specialists—such as organizing schedules or managing emails.AI Agents excel at automating simple, repetitive tasks but lack the autonomy or decision-making capabilities of Agentic AI(as shown in the image below).

Understanding Agentic AI, Generative AI, and AI Agents

AI Agents can be understood as virtual assistants like Siri, which act strictly according to your instructions but do not think for themselves. The autonomy of AI Agents is more limited.

Practical Applications of AI Agents

  1. Customer Support: The most common use of AI Agents is in customer service, appearing in the form of chatbots that can answer questions, solve problems, and guide customers through processes—all without human intervention. For example, Alipay’s Zhixiaobao and China Mobile’s Lingxi.
  2. Personal Assistants: Voice assistants like Siri or Google Assistant can help users set alarms, check the weather, or play music—tasks that do not require much decision-making. This type of operation relies on predefined commands, showcasing how AI Agents excel at handling simple, repetitive tasks.
  3. Email Management: As mentioned earlier, AI Agents can categorize emails, flag important ones, and even provide smart replies to save time. For instance, Google’s Gmail smart compose feature—suggesting phrases based on context to help users respond to emails more quickly.
  4. Productivity Tools: Tools like Copilot can provide support by suggesting code and helping with debugging. They act like a second pair of eyes on standby, enhancing developers’ efficiency by providing real-time code suggestions, allowing them to focus on other more creative aspects of their work.

Conclusion

Understanding the differences between Agentic AI, Generative AI, and AI Agents can help us better utilize AI tools, improve efficiency, and adapt to the future.
In summary, Generative AI focuses on “generation”. It is very useful in tasks such as text generation, responding to queries by generating text or images, but is limited to following instructions without true autonomy.
On the other hand, Agentic AI is a step forward—it can set goals, make decisions, and adapt to changing situations independently, handling complex tasks without the need for continuous human guidance.
By using methods like the Agentic workflow, AI systems can become more efficient. They improve performance through iterative steps and learn from each stage. This transition opens up opportunities for advanced applications and allows even older models to continuously evolve.
As AI continues to develop, the boundaries between Agentic AI and AI Agents may further blur—AI Agents may learn and adapt like Agentic AI, providing more powerful automation and decision-making capabilities. Generative AI may also develop greater autonomy and adaptability; Agentic AI may become more complex and intelligent.Understanding Agentic AI, Generative AI, and AI Agents
*Main information source: https://medium.com/

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