Diving into the differences between agentic AI and generative AI means first defining both.
These models work by identifying and encoding the patterns and relationships in huge amounts of data, and then using that information to understand users' natural language requests or questions. These models can then generate high-quality text, images, and other content based on the data they were trained on in real-time.
Agentic AI describes AI systems that are designed to autonomously make decisions and act, with the ability to pursue complex goals with limited supervision. It brings together the flexible characteristics of large language models (LLMs) with the accuracy of traditional programming. This type of AI acts autonomously to achieve a goal by using technologies like natural language processing (NLPs), machine learning, reinforcement learning and knowledge representation. It’s a proactive AI-powered approach, whereas gen AI is reactive to the users input. Agentic AI can adapt to different or changing situations and has “agency” to make decisions based on context. It is used in various applications that can benefit from independent operation, such as robotics, complex analysis, and virtual assistants.
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Learn how goal-driven and utility-based AI adapt to workflows and complex environments.
Agentic AI and generative AI have objectives and distinct attributes that make them unique from one another.
Content creation: Where gen AI excels is in content generation. The AI models can create coherent context like essays and answers to complex problems. AI applications, like OpenAI’s ChatGPT can generate answers, write lists, and give advice when prompted by user input. Using gen AI solutions to produce code can streamline software development and make it easier for developers of varying skill levels to write code.
Data analysis: Generative AI can analyze vast amounts of data and use that analysis to discover patterns and trends. The gen AI models can streamline complex workflows, especially when it comes to the supply chain and drive a better customer experience.
Adaptability: Gen AI can adapt its outputs based on the input it receives from the user. If the user is providing specific feedback to the model the outcome shifts to align more to what the user is seeking and in turn refine the output.
Personalization: Gen AI technology can make personalized recommendations and experiences based on the inputs from the user. The retail industry has, for example, taken to highly personalized experiences for their customers thanks to gen AI technology that is helping them understand every detail of their customer preferences.
Decision-making: Because of the pre-defined plans and objectives these AI systems can assess situations and determine the path forward without or with minimal human input.
Problem-solving: Agentic AI uses a four-step approach for solving issues; perceive, reason, act, and learn. These four steps start by having AI agents gather and process data. The LLM then acts as an orchestrator that analyzes perceived data to understand the situation. And is then integrated with external tools that are continuously improving and learning through feedback.
Autonomy: Autonomous behavior defines agentic AI. It’s unique ability to learn and operate on its own make it a promising technology for organizations seeking to streamline workflows and have machines perform complex tasks with minimal human intervention.
Interactivity: Due to its proactive nature, agentic AI can interact with the outside environment and gather data to adjust in real-time. One example is self-driving vehicles, which must constantly analyze its surroundings and make safe, accurate driving decisions.
Planning: Agentic AI models can handle complex scenarios and execute multi-step strategies to achieve specific goals.
Agentic AI is the broader concept of solving issues with limited supervision, whereas an AI agent is a specific component within that system that is designed to handle tasks and processes with a degree of autonomy. This model is changing the way humans interact with AI. The agentic AI system is able to understand the goal or vision of the user and uses the information that is provided to solve a problem.
To put this in an example, think of a smart home where agentic AI manages and runs the overall energy consumption system. This is done by using real-time data and user preferences to coordinate individual AI agents like the smart thermostat, the lighting or even appliances. The agents have individual goals and assignments, and work together within the agentic AI framework to achieve the homeowner’s energy goals.
There are many use cases for generative AI, however many applications of agentic AI are still in the experimental phase. Potential agentic AI uses cases are emerging in functions like customer service, healthcare security, workflow management and financial risk management.
Businesses are using gen AI to produce large volumes of SEO-optimized content, such as blogs and landing pages that help drive organic traffic. For instance, a digital marketing agency might use gen AI tools to create high-quality, keyword-optimized blog posts or web pages for their clients to rank higher on search engines.
The gen AI capabilities available can help organizations in creating new product concepts or designs based on market research, trends, and user preference. Which might in turn speed up the product development cycle. An example is a fashion company by using gen AI to design a new clothing line and generating designs based on consumer input and market data analysis.
Gen AI can help companies automatically generate responses for customer service inquiries. The tools can craft answers for common questions and troubleshoot issues in real-time. Take an ecommerce business for instance. It can use gen AI in chatbots to handle many tasks, such as order status inquiries, refund requests, and shipping questions.
The traditional models for customer chatbots had limitations due to the pre-programmed nature of the technology and would require human intervention at times. Whereas with autonomous agents, the model can quickly understand what a customer's intent and emotion is and take steps to resolve the issue.
AI technology has been used in the healthcare field already, including in diagnostics, patient care, and streamlining administrative tasks. Cybersecurity is one of the most vital features of any AI tool that is used in the healthcare space due to patient data and privacy concerns. This concern carries over into emerging agentic AI tools as well.
Agentic AI can manage business processes autonomously and handle complex tasks like reordering supplies and optimizing supply chain operations. It can automate internal workflows to make it easier on human employees without the need for their physical intervention.
For example, a logistics company might use an agentic AI system to automatically adjust delivery routes and schedules based on real-time traffic conditions and shipment priorities. The scalability and increased capacity of agentic AI also makes it a good use case for the logistics industry specifically.
Agentic AI can help industries meet client goals and optimize the results in real-time by analyzing market trends and financial data to make autonomous decisions about investments and credit risks. Financial institutions are looking to protect their clients' investments while also making smart and strategic decisions that result in higher returns.
Agentic AI can improve those practices by acting autonomously and adjusting strategies based on real-time economic, social and political events. An example is a fintech firm that uses agentic AI to monitor market fluctuations and automatically adjust portfolio allocations.
Ecosystems, libraries, and foundations to build on. Orchestration frameworks, agent platforms, and development foundations.
