Agentic AI
Agentic AI can be a proactive business partner that saves your organization time, labor, and money. Learn how AI agents drive real-time, data-driven decisions and transform operations in this guide from edX.
By: Shelby Campbell , Edited by: Valerie Black
Last updated: December 11, 2025
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can implement processes to achieve objectives without ongoing human prompting. While they require initial guidelines set by humans, agentic AI systems can apply logic and feedback to continuously adapt their approach and contribute to the overall goal.
In a 2024 LinkedIn post, AI researcher and founder of DeepLearning, Andrew Ng, defined four agentic AI design patterns:
- Reflection: Assess and improve upon past performance.
- Tool use: Use tools to process data and complete tasks.
- Planning: Create and follow a step-by-step plan that evolves with changing circumstances.
- Multi-agent collaboration: Employ several agents to complete necessary incremental tasks.
Agentic AI offers many use cases to streamline processes and reduce labor requirements across industries. For example, a supply chain manager can implement an agentic AI system to monitor inventory levels, coordinate shipments and deliveries, and even trigger stock reorders with minimal human prompting.
What sets Agentic AI apart from other AI tools?
The most common AI tools, such as ChatGPT, Claude, and Gemini, are all generative AI. These systems react to a prompt with a direct output. Agentic AI, however, doesn't just respond to an initial prompt — it generates and completes follow-up actions that contribute to your overarching goal.
How Agentic AI improves business efficiency
Businesses, both small and large, have begun investing in and shifting their processes around AI. However, a March 2025 McKinsey report shows that while nearly 80% of companies have implemented some sort of generative AI process, over 80% of those companies report that the technology has had no impact on earnings.
Agentic AI may have the capabilities necessary to close this return-on-investment gap. Where generative AI requires reviewing and re-prompting one task at a time, agentic AI can automate an entire process.
An AI agent doesn't need a human to make ongoing decisions about how to proceed. Instead, it makes instantaneous, data-driven decisions about what needs to happen to accomplish an overarching goal, ultimately requiring less labor and cutting down the time necessary to complete a project or process.
The future of Agentic AI
AI's rapid advancement continues to unfold across industries. One of the most promising areas for growth lies in open source models, which provide researchers and companies with libraries of data they can use to train specialized AI agents.
Using foundation models like NVIDIA's Nemotron and OpenAI's GPTs, organizations develop specialized AI agents that leverage this open-source data, guardrails, and specific instructions to accomplish tasks. These models also learn from experience and adjust for better results in the future.
As each model matures, new use cases emerge. Therefore, an increasing number of businesses will likely introduce new forms of agentic AI for public use in the next couple of years.
Perplexity's PayPal purchasing agent is a notable example of how companies are integrating open foundation models. As an agentic model, it can automatically choose and purchase products or services through the platform, as well as employ agents tho secure each transaction.
Other recently launched agentic AI products include: