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You might have heard terms like ‘agentic AI’ and ‘generative AI’ floating around without fully understanding their meaning. For many, AI represents a steep learning curve, with a number of terms (like LLM and ChatGPT) rapidly becoming part of the zeitgeist, even though these words wouldn’t have meant much to most of us before 2021.
The popularity and widespread use of these generative AI tools have cemented that many industries are ready for the more autonomous agentic AI. In fact, the global agentic AI market was valued at around USD 5.2 – 5.3 billion in 2024, and is projected to reach USD 52.6 billion by 2030. What’s more, one report predicts that 15% of day‑to‑day work decisions will be autonomously made by agentic AI by 2028, and 33% of enterprise software applications will include agentic AI by 2028.
Both agentic AI and generative AI are evolving AI paradigms with different capabilities and different purposes. But, what is agentic AI vs generative AI? How do they differ, and where do they overlap? To make the most of emerging technologies (and avoid being left behind), it’s essential to understand their use cases.
One recent survey found that 74% of organisations say their investments in generative AI and automation have met or exceeded expectations. If you’re ready to streamline your business, minimise time to value and maximise probability of project success, contact us today.
Agentic AI refers to artificial intelligence systems that can autonomously perform tasks, make decisions, and execute complex actions over time, often with the goal of achieving specific objectives. These systems are designed to act independently, adapt to changing conditions, and continuously refine their strategies based on ongoing feedback and new data.
Agentic AI is the AI we might have imagined in Sci-Fi movies twenty years ago— it’s not living and breathing, but it is coming to decisions independently. Agentic AI doesn’t rely on prompts and input from a user in the way that Gemini or ChatGPT does. This kind of AI is programmed to be an autonomous agent that plans, reasons, and executes across long timeframes.
Agentic AI is particularly effective in use cases that require workflow automation, and multi-step task execution. Agentic AI analyses real-time data and adjusts its actions to optimise outcomes, making decisions based on changing conditions.
Generative AI uses models like transformers (for example, GPT-3) or generative adversarial networks (GANs) to generate original outputs that mimic patterns or styles in the data it was trained on. Its applications range from creating realistic images and videos to generating human-like text and even solving creative challenges.
Generative AI is likely the technology you are most familiar with, as this refers to the most publicly available content generation tools like ChatGPT, DALL·E, Midjourney, and Claude. These tools are capable of rapid and comprehensive content generation, including text, images, and code. Currently, these tools require quality prompts, constant guidance, and access to vast swathes of human-made content in order to function. The autonomy and planning, as well as independent reasoning, is limited.
| Feature | Agentic AI | Generative AI |
| Purpose | Designed for autonomy, planning, and execution of tasks with an emphasis on achieving specific goals over time. | Primarily focused on content generation based on user inputs, with a focus on creating text, images, or code. |
| Examples | AutoGPT, ReAct agents, autonomous robots, self-managing systems. | ChatGPT, DALL·E, GPT-3, Stable Diffusion, MidJourney. |
| Autonomy | High – Can operate independently with minimal intervention, making decisions and adjustments as needed over time. | Low – Responds to prompts with one-shot responses, lacking long-term persistence or goal-driven decision-making. |
| Interactivity | Multi-step, goal-oriented processes; designed to interact with multiple systems or take actions over an extended period. | One-shot responses; focuses on generating content based on a single user input without follow-up tasks or long-term objectives. |
| Decision-making | Persistent, task-based decision-making; it adapts based on ongoing inputs, continuously refining actions to reach goals. | Prompt-to-response decision-making; generates a response based on a given prompt with no continuation of decision-making after the response. |
| Use Cases | Workflow automation, task orchestration, virtual agents, personal assistants, autonomous systems, robotics, digital agents that perform tasks. | Text/image/code generation, creative writing, art, design, programming help, language translation, and problem-solving. |
| Complexity of Tasks | Handles complex, multi-phase tasks that require sustained effort, adjustments, and context retention over time. | Simpler, single-phase tasks, such as generating a single article, image, or code snippet in response to a prompt. |
| Learning and Adaptability | Can learn and adapt over time to better handle tasks, optimize workflows, and refine strategies. May improve over time based on previous actions. | Generally static learning (pre-trained models) that doesn’t adapt during interactions; new learning requires retraining the model. |
| System Integration | Can interact with and control multiple systems and tools to execute complex plans autonomously across different environments. | Interacts with the user to generate responses based on input, but does not control systems or execute tasks beyond content generation. |
| Examples of Goals | Example: Autonomously handle customer support tickets, manage supply chain tasks, or create and maintain a personal knowledge base. | Example: Generate a blog post, create an image based on a prompt, or write code snippets. |
| Core Technology | Often relies on reinforcement learning, multi-agent systems, or planning algorithms to achieve autonomy. | Typically uses transformer-based models (like GPT-3, BERT) for generating content based on vast datasets of pre-existing information. |
Autonomous vehicles, like those developed by Waymo, rely on agentic AI to navigate roads, make decisions about speed, braking, and route selection, and handle complex traffic. The system learns from its environment (via cameras, radar, and LIDAR) in real-time, adapting to road conditions, and user preferences.
AutoGPT is an advanced form of AI that can autonomously manage tasks, research, generate content, and even make decisions based on predefined goals. For example, AutoGPT can plan a trip by autonomously searching for flights, booking accommodations, and creating an itinerary based on the user’s preferences.
The system can interact with multiple sources of data, manage various tasks simultaneously, and adapt its strategy depending on the progress of the task and the information it encounters.
UiPath is a leader in robotic process automation (RPA) that uses agentic AI to automate repetitive business processes. This includes tasks like invoicing, data entry, scheduling, and report generation. The AI system is capable of executing workflows end-to-end, interacting with multiple systems (including ERP systems, email, and spreadsheets) to process business tasks autonomously.
Renaissance Technologies, a hedge fund, uses sophisticated algorithmic trading systems powered by AI to autonomously trade stocks. These systems analyse vast amounts of market data, execute trades, and adjust strategies based on changing conditions.
DJI’s agricultural drones use AI to autonomously monitor crops and apply fertilisers. These drones can fly over fields, analysing plant health using sensors and imaging technologies. The AI system optimises spraying and watering to improve crop yield while minimising the use of resources.
| Feature | Agentic AI | Robotic Process Automation (RPA) |
| Purpose | Autonomous decision-making, multi-step task execution, workflow automation. | Automates repetitive, rule-based tasks that follow predefined workflows. |
| Complexity of Tasks | Handles complex, multi-phase tasks that require decision-making and adaptation over time. | Primarily used for simple, repetitive tasks that follow set rules or scripts. |
| Intelligence Level | High – Can adapt, learn, and make decisions autonomously based on real-time data. | Low to Medium – Operates based on predefined scripts and lacks adaptive learning. |
| Autonomy | High – Operates independently, continuously refining actions to achieve goals. | Low – Operates based on rigid, rule-driven processes and lacks independent decision-making. |
| Examples | Autonomous vehicles, virtual assistants, personal agents (e.g., AutoGPT). | Data entry automation, invoice processing, customer service chatbots. |
| Interactivity | Multi-step, goal-oriented, can interact with multiple systems to achieve long-term objectives. | Single-step, process-oriented, often involves simple tasks like filling forms or responding to emails. |
| Use Cases | Workflow automation, project management, intelligent agents, autonomous systems in various industries. | Automating business processes like payroll, data extraction, and customer queries. |
| Adaptability | Can learn and adapt to changing conditions and new information. | Limited adaptability; follows predefined instructions without learning from new data. |
| Decision-Making | Persistent decision-making based on ongoing data and task evolution. | Rule-based decisions that are static and do not change unless manually updated. |
| Integration | Integrates across complex systems and makes real-time decisions based on interactions. | Typically integrates with existing enterprise systems but doesn’t handle complex decision-making or integrations. |
The question of agentic vs generative AI remains.
Whether Agentic AI will replace Generative AI often stems from the idea that these technologies might be in competition, but in reality, they’re more complementary than rival systems. While Agentic AI is designed to handle autonomous decision-making and execute complex, multi-step tasks, Generative AI excels at creating content based on user input.
In fact, Generative AI can actually power Agentic AI, by using LLMs within an agentic system to generate real-time responses or content as part of a larger task. This fusion allows agentic systems to have the creativity and flexibility of Generative AI while maintaining their ability to handle complex decision-making and multi-step actions.
Looking ahead, we can expect a future trend where these technologies merge, creating intelligent agents that leverage both decision-making abilities and content generation. Imagine an agent that can autonomously handle a project, make decisions, and generate the necessary content (emails, reports, designs) all in real-time—powered by Generative AI but guided by Agentic AI’s autonomy.
