{"id":134361,"date":"2025-09-18T06:16:00","date_gmt":"2025-09-18T06:16:00","guid":{"rendered":"\/tutorials\/?p=134361"},"modified":"2026-03-09T19:16:14","modified_gmt":"2026-03-09T19:16:14","slug":"what-is-agentic-ai","status":"publish","type":"post","link":"\/ng\/tutorials\/what-is-agentic-ai","title":{"rendered":"What is agentic AI? Definition, examples, and how it works"},"content":{"rendered":"

Agentic AI <\/strong>is an advanced form of artificial intelligence that can independently set goals, plan, reason, and take action to achieve a specific objective with minimal human oversight.<\/p>

Unlike traditional AI, which is reactive and responds to direct commands, agentic AI is proactive and can initiate tasks on its own. It is characterized by its autonomy, goal-orientation, reasoning, adaptability, and collaborative capabilities.<\/p>

To help you grasp the cutting-edge of AI, we’ll break down agentic AI<\/strong>, explaining how it plans, reasons, and acts to achieve goals. Discover practical examples of its use in business, finance, and healthcare, and learn exactly how it differs from generative AI to understand which is the right tool for the job.<\/p>

\n\n\n\n<\/p>

What is agentic AI?<\/strong><\/h2>

Agentic AI is a type of artificial intelligence that reacts to commands and actively takes initiative to accomplish specific goals.<\/p>

While traditional AI systems are designed to carry out predefined tasks in response to clear input (like answering questions or sorting data), agentic AI operates with a high degree of autonomy<\/strong>. It can function independently, perceive its environment, analyze data, “reason” about the next action, and act to achieve a goal without needing constant human intervention.<\/p>

To put it into perspective, imagine a self-driving car. Traditional AI would simply follow pre-programmed commands, such as “navigate from point A to point B.”<\/p>

Agentic AI goes a step further. It’s capable of analyzing changes in the environment (for example, road conditions or traffic patterns), reasoning potential obstacles, and adapting its behavior (like rerouting or slowing down) to reach the destination safely.<\/p>

\n

🤖 Agentic AI vs AI agent<\/h4>\n

Despite their similar names, agentic AI<\/strong> is different from AI agents<\/a>. AI agents are built upon reactive AI, designed and trained to complete a specific, well-defined task. They don’t have the ability to analyze dynamic data and adjust their actions autonomously based on the outcome.<\/p>\n <\/div>\n\n\n\n<\/p>

This shift from a reactive<\/strong> model (where AI only reacts to input) to a proactive<\/strong> one (where AI takes independent action to achieve an outcome) is one of the defining features of agentic AI.<\/p>

Whether optimizing business processes or managing a smart home, agentic AI doesn’t just follow instructions – it anticipates needs<\/strong>, solves complex problems, and learns from experience to improve its performance over time.<\/p>

How does agentic AI work?<\/strong><\/h2>

To understand how agentic AI works, think of it as a cycle of perception, reasoning, action,<\/strong> and learning<\/strong>. This feedback loop allows the AI to function autonomously, make decisions, and take actions that bring it closer to its goal. Let’s explore each stage in detail:<\/p>

1. Perception<\/strong><\/h3>

This is the first stage, where the AI “perceives” its environment by collecting data<\/strong> from its surroundings. It could use hardware or fetch data from external systems by setting up the model context protocol (MCP) and application programming interface (API)<\/a>.<\/p>

In a smart factory, for example, the AI might collect real-time data on machinery conditions. Meanwhile, agentic AI in a self-driving car gathers data through cameras, LiDAR, and radar to understand the road and its surroundings.<\/p>

2. Reasoning<\/strong><\/h3>

Once the data is collected, the AI processes it to understand the situation. It breaks down complex problems into smaller, manageable tasks.<\/p>

For example, an AI tasked with optimizing a supply chain might receive data about current stock levels, supplier delays, and customer demand. The AI then analyzes this information and determines the best course of action, such as adjusting production schedules or finding alternative suppliers.<\/p>

An agentic AI on a self-driving car, on the other hand, may use data about the current traffic situation, road conditions, and weather.<\/p>

3. Action<\/strong><\/h3>

After reasoning through the data, the AI takes actions to achieve the goal. In the supply chain example, this could involve rerouting a shipment, making a recommendation to users, or activating a system (such as turning on a machine or adjusting a thermostat).<\/p>

Meanwhile, in an autonomous vehicle, this stage involves adjusting the car’s speed, changing direction, and stopping when necessary.<\/p>

4. Learning<\/strong><\/h3>

The final stage of the agentic AI cycle involves learning from the previous actions’ outcomes. It assumes questions like:<\/p>