As 2025 began, many people expected it to be a year of agentic AI: systems that can take actions toward a goal. This article explains the concept and the role it may play in the changing field of AI.
What is agentic AI?
Agentic AI refers to AI designed to work toward a goal. It can make decisions and take actions to pursue that goal. Compared with a system that only responds to each prompt, an agentic system may carry out multiple steps without continuous human direction, and may adjust its actions based on what happens.
A simple AI hierarchy
These related concepts can be understood as a progression:
- Artificial intelligence (AI): systems designed to perform tasks associated with human intelligence.
- Machine learning: AI methods that learn patterns from data.
- Deep learning: machine learning that uses multi-layer neural networks.
- Generative AI: AI that can create new material such as text, images, or audio.
Generative AI and agentic AI
Generative AI focuses on creating output in response to an instruction, such as producing an image from text or drafting an article. Agentic AI focuses on completing a goal, with examples such as planning a trip, managing tasks, or trading stocks.
A four-stage agent workflow
The article describes a four-stage process:
- Perceive: gather information from the environment.
- Reason: analyze the information and plan.
- Act: carry out the plan.
- Learn: use the outcome to adjust future work.
Design patterns for agentic AI
- Reflection: the agent evaluates its own work.
- Ping: the agent sends a signal to check status.
- Tools: the agent uses tools to complete tasks.
- Multi-agent: multiple agents work together.
Examples and possible uses
Examples in the article include AI agents for tasks such as answering chats or managing email, along with systems for automated trip planning, task management, and stock trading.
Agentic AI may have a growing role in everyday life and business, including human-resources management, product development, marketing, and customer service. The article forecast rapid progress in 2025.
Related AI concepts
Artificial intelligence and machine learning
AI describes systems designed for tasks associated with human intelligence. Machine learning is a field within AI that develops algorithms able to learn from data without being explicitly programmed for every rule.
Deep learning and generative AI
Deep learning uses neural networks with multiple layers to analyze data. Generative AI can create new material, including text, images, and audio.
GANs and Transformers
A Generative Adversarial Network (GAN) uses two networks: one generates data and the other attempts to distinguish generated data. A Transformer is a neural-network architecture used in natural-language processing and other tasks involving sequential data.
AI agents and multi-agent systems
An AI agent is an AI system designed for a particular task, such as responding to chats or managing email. In a multi-agent pattern, several agents work together.
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