Agentic AI refers to systems comprising one or more AI agents that perceive their environment, utilize AI models (such as large language models or other generative models) to reason, plan, and make decisions, take actions, and complete complex tasks with varying degrees of autonomy.

Other AI approaches, including earlier forms of machine learning, often perform narrow, predefined tasks based on fixed rules or patterns. For example, they might excel at image recognition or text generation but lack the ability to plan and adapt to dynamic situations. While generative AI can create complex outputs like text and images, agentic AI systems are specifically designed to enable agents to operate more independently. These agents perceive their environment, employ AI models (including generative AI models) for reasoning and decision-making to determine appropriate actions, and then execute those actions to achieve complex goals. Ideally, they can also learn and adapt over time, improving their performance.

Not necessarily. While tech professionals can build complex multi-agent systems using code, there are also increasingly accessible no-code or low-code solutions. This means that individuals from various backgrounds, whether you're a business leader aiming to evaluate and spearhead AI initiatives, or a non-technical expert seeking to integrate agentic AI into your daily workflows and professional practice, can find suitable ways to learn and apply this technology.

Agentic AI is already being used to automate customer support, manage complex scheduling, streamline internal operations, power intelligent assistants, and scale decision-making across industries like finance, healthcare, retail, and more. It's redefining productivity by taking on multi-step tasks that previously required human coordination.