Agentic AI and Generative AI represent two powerful and rapidly evolving branches of artificial intelligence. As supply chains begin to leverage Artificial Intelligence (AI), it is important to understand the technology’s foundation.
Artificial intelligence (AI) is a rapidly evolving field transforming industries, economies, and how we live. While AI generally refers to machines designed to simulate human intelligence, there are distinct branches within the field that focus on specific tasks and goals. Two important categories within AI are Generative AI and Agentic AI, which are increasingly prevalent in various applications. This article will define both types of AI, explain their differences, and provide examples to help clarify their capabilities, applications, and potential future implications.
What is Generative AI?
Generative AI refers to a class of machine learning models and algorithms designed to generate new, original content. Whether that content is in the form of text, images, music, videos, or even more complex media, the hallmark of generative AI is its ability to create new outputs based on patterns and knowledge extracted from existing data.
The key principle behind generative AI is that it learns from a vast amount of input data and generates outputs that resemble the data it was trained on, but which are not direct copies. Essentially, generative AI “understands” the underlying structure and characteristics of the data and can create new instances that mirror those patterns.
How Does Generative AI Work?
Generative AI systems generally rely on machine learning techniques, particularly deep learning, and often use models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer-based models. These models require large datasets to train, and the more diverse and comprehensive the dataset, the better the AI becomes at producing high-quality content.
In a Generative Adversarial Network (GAN), for example, two neural networks work against each other: the generator, which creates new content, and the discriminator, which evaluates the content’s authenticity. The generator’s goal is to create content that the discriminator cannot distinguish from real data, pushing the generator to improve over time.
In Transformer models, like GPT (Generative Pre-trained Transformer), a machine learning model is pre-trained on vast amounts of text data to understand context, grammar, structure, and meaning. It then uses this learned information to generate coherent and contextually relevant text based on a given prompt.
Examples of Generative AI
- Text Generation: One of the most well-known examples of generative AI is OpenAI’s GPT-3 and GPT-4. These models are trained on massive amounts of text data from books, websites, and other sources to produce text that mimics human writing. These models can generate essays, articles, poetry, dialogue, and more, making them incredibly versatile. For instance, an individual could ask GPT-3 to write a short story on a specific theme or summarize a lengthy article.
- Image Generation: Another well-known example is DALL-E, also from OpenAI. DALL-E uses a type of deep learning called a transformer to generate images based on textual descriptions. For instance, a prompt like “an astronaut riding a horse in a futuristic city” would lead DALL-E to generate a unique image that fits that description, drawing on its learned understanding of the images of astronauts, horses, and futuristic cities.
- Music Creation: Aiva is an AI system that uses generative music composition techniques. Trained in a dataset of classical music, it can generate new compositions that are original yet stylistically like the work of classical composers like Bach, Beethoven, and Mozart.
- Video Generation: Tools like RunwayML and Synthesia are pushing the boundaries of AI-generated video. These platforms can create videos with synthetic actors or generate animations corresponding to a text prompt. For example, Synthesia allows users to input text, and the AI creates a video of a human-like avatar speaking the text, which can be used for training videos, marketing, or personalized content.
Use Cases for Generative AI
Generative AI has a wide range of applications across different industries. Some of the key use cases include:
- Creative Industries: Artists, musicians, and writers use generative AI to create new content. It can help brainstorm ideas, compose music, write scripts, or generate new pieces of art. In the film industry, generative AI is being used to generate special effects, characters, and even scripts.
- Marketing and Advertising: Generative AI is increasingly being used in marketing to create personalized advertisements, product descriptions, and content that resonates with customers. It can automatically generate copy for ads, landing pages, and social media posts, saving marketers time and increasing their productivity.
- Healthcare: In healthcare, generative AI models can assist in drug discovery by simulating how molecules will behave in various environments or generating entirely new compounds that could effectively treat diseases.
- E-commerce: In e-commerce, companies can use generative AI to create personalized product designs or logos based on customer preferences. It can even suggest new products based on consumer behavior and trends.
What is Agentic AI?
Agentic AI, also known as Autonomous AI or Autonomous Agents, refers to AI systems designed to take autonomous actions worldwide to achieve a specific goal or set of objectives. Unlike generative AI, which focuses on creating content, agentic AI interacts with the environment, makes decisions, and performs tasks without direct human intervention.
Their autonomy and goal-oriented behavior typically characterize agentic AI systems. They are often used to interact with physical or virtual environments in real-time, responding to changes, making decisions, and learning from feedback to improve performance over time. These systems are typically powered by reinforcement learning (RL), a type of machine learning where an agent learns to take actions that maximize some notion of cumulative reward.
How Does Agentic AI Work?
Agentic AI operates in an environment, continually receiving input from sensors or other data sources. It then uses this input to make decisions or take actions to achieve a set of objectives or maximize a specific reward function. Over time, agentic AI systems use feedback loops to improve their decision-making capabilities.
In reinforcement learning, an agent interacts with an environment and receives feedback through rewards or penalties based on its actions. The agent then adjusts its behavior to maximize the total reward over time. This is akin to how humans learn from experience, where positive outcomes reinforce certain behaviors and negative outcomes discourage them.
Examples of Agentic AI
- Autonomous Vehicles: Self-driving cars, like those developed by Waymo, Zoox, and Tesla, are prime examples of agentic AI. These cars use sensors, cameras, and machine learning algorithms to perceive their environment and make decisions about navigating safely. The AI continuously adapts its actions based on real-time data from its environment, adjusting the vehicle’s speed, steering, and braking to avoid collisions and follow traffic laws.
- Robotic Process Automation (RPA): In industries like manufacturing and logistics, robotic systems are increasingly used to perform repetitive tasks like picking and packing, sorting, and assembling. These robots, powered by agentic AI, operate autonomously in warehouses and factories, reducing labor costs and improving efficiency.
- AI Personal Assistants: Virtual assistants like Amazon’s Alexa, Google Assistant, and Apple’s Siri are agents of Agentic AI that respond to user commands. These systems use natural language processing and contextual understanding to interpret voice commands and perform specific actions, such as setting reminders, sending texts, or controlling smart home devices.
- Gaming AI: Agentic AI controls non-player characters (NPCs) and enemies in video games. These characters react to the player’s actions, learn from past interactions, and can even collaborate or compete with each other in multiplayer games. Advanced AI in games like StarCraft II or Minecraft allows NPCs to adapt to the player’s strategies, providing more dynamic and challenging gameplay.
- Financial Trading Algorithms: Agentic AI is widely used in finance for high-frequency trading and portfolio management. These algorithms make real-time decisions about buying and selling stocks based on market data, trying to maximize profit by reacting faster than human traders. Over time, these systems learn which strategies yield the best returns.
Use Cases for Agentic AI
Agentic AI can be deployed in a variety of fields, including but not limited to:
- Autonomous Vehicles and Transportation: Self-driving cars, trucks, and drones for package delivery are examples of agentic AI in the transportation sector. They can move goods and people autonomously, reducing human intervention and improving efficiency.
- Healthcare and Surgery: Agentic AI can assist in surgeries through robotic surgery systems or even diagnose medical conditions. In hospitals, AI-driven robots increasingly perform tasks like delivering supplies, managing patient records, or assisting with rehabilitation.
- Supply Chain and Logistics: Autonomous drones and robots are revolutionizing supply chains, automating inventory management, sorting packages, and delivering products. These systems optimize the supply chain by reacting dynamically to real-time conditions and adapting their behavior accordingly.
- Customer Service: In customer service, AI-driven chatbots and virtual assistants autonomously respond to customer queries, troubleshoot problems, and even process transactions. These systems can interact with customers through voice or text, handling routine tasks and escalating complex issues to human agents.
Key Differences Between Generative AI and Agentic AI
While Generative AI and Agentic AI are integral parts of the artificial intelligence ecosystem, they fundamentally differ in their goals, functionality, and applications. Here are the primary distinctions:
Purpose
- Generative AI is focused on creating new content, whether text, images, music, or other forms of media. Its purpose is to produce something new based on existing data without the need for real-time interaction with the physical world.
- Agentic AI, conversely, is focused on taking autonomous actions to achieve a goal. It interacts with its environment, makes decisions, and performs tasks based on real-time data and feedback.
Interaction with the Environment
- Generative AI does not typically interact with its environment in real-time. It takes in data (like text or images), processes it, and produces output based on learned patterns.
- Agentic AI constantly interacts with its environment, sensing changes and adapting its actions accordingly. It performs tasks and makes decisions in real-time, reacting to dynamic conditions.
Learning Methods
- Generative AI generally learns by mimicking patterns in large datasets. It is trained on massive data to learn the structure and style of the content it generates.
- Agentic AI often learns through reinforcement learning, where it learns by trial and error, receives feedback about its actions’ success or failure, and adjusts its behavior accordingly.
Output
- Generative AI produces content, such as text, images, music, or video.
- Agentic AI produces actions like driving a car, managing inventory, or diagnosing a medical condition.
In summary, Generative AI and Agentic AI represent two powerful and rapidly evolving branches of artificial intelligence. Generative AI focuses on producing new content by learning from existing data. At the same time, Agentic AI is designed to perform tasks autonomously, making decisions and taking actions based on real-time data.
These two AI types are complementary in many ways. For example, generative AI can create content, while agentic AI can take actions based on that content. Both types are expected to continue transforming industries, with generative AI enhancing creativity and content generation while agentic AI automates processes and improves operational efficiency.
As both technologies evolve, we can expect them to converge in new and exciting ways, creating even more sophisticated and integrated systems that will impact everything from healthcare to entertainment, transportation, supply chains, and beyond. Whether creating content or taking action, generative and agentic AI are set to play key roles in shaping the future of technology and human society.
