What is agentic AI?

Agentic AI is a software system designed to interact with data and tools in a way that requires minimal human intervention. With an emphasis on goal-oriented behavior, agentic AI (also known as AI agents) can accomplish tasks by creating a list of steps and performing them autonomously.

You can think of agentic AI as a way of combining automation with the creative abilities of a large language model (LLM). To bring agentic AI to practice, you create a system that provides an LLM with access to external tools, and algorithms that supply instructions for how the AI agents should use those tools.

The way agents communicate with tools involves orchestration, with flows or graphs depending on the framework being used. This approach allows the LLM to “reason” and determine the best way to answer a question–such as deciding whether the query can be answered with available information or whether an external search is necessary.

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Think of an AI agent as an entity that sits on top of other software tools and operates them. Agentic AI can be a physical structure, a software program, or a combination of the 2.

An AI agent in a robotic system might use cameras, sensors, and monitors to collect data about its environment, then run that information alongside software to make determinations about its next step. This is a process known as sensor fusion.

Meanwhile, agentic AI in a software setting would collect data from other sources, such as APIs, online searches, text prompts, and databases that help the agents create a sense of perception and context.

Let’s dig a little deeper into some of the specialized features of agentic AI:

Agentic AI is adaptive and dynamic

Agentic AI learns from previous patterns and data. This means that it can change its strategy based on new or changing information it receives, in real time. While traditional workflows only move forward, agentic workflows can move forward and backward, with the ability to backtrack and mend errors as it goes. In other words, agentic AI can proactively anticipate needs and reflect on its own work.

For example, an autonomous vehicle may use agentic AI to improve its ability to sense the difference between a piece of trash on the road and a squirrel. As it continuously monitors and analyzes its own behavior, it can improve the outcome of its actions.

Agentic AI can independently manage and complete tasks

Agentic AI is sometimes referred to as autonomous AI. This is because it has the capability to communicate and collaborate with other AI systems and digital infrastructures on behalf of a human user, or another AI agent.

For example, you can tell an AI agent that you want to make spaghetti for dinner. The AI agent could then complete the steps necessary to find a recipe, make a list of ingredients, and place an order for those ingredients to be delivered to your home from a local grocery store.

Agentic AI has a “chaining” ability

This means that the AI system can perform a sequence of actions in response to a single request. For example, if you ask an AI agent to “create a website” it can perform all the steps needed to carry out that task. This means that from 1 prompt, the AI agent can carry out the tasks of writing the code for the structure, populating the pages with content, designing the visuals, and testing for responsiveness.