Agentic AI: What's Next in AI

Note: This article was last updated on 8/19/2025. 

Key Takeaways: 

• Agentic AI, the next progression in AI, has the autonomy to make decisions and take actions on its own. 
• Agentic AI will be able to help automate workflows, serve customers more effectively, and make informed decisions. 
• But agentic AI will present its own challenges with security, governance, and lack of AI-ready data. 

For the past few years, generative AI has been the buzzword. But now, as technology has progressed, a new evolution has arrived: agentic AI.

What Is Agentic Artificial Intelligence (AI)? 

Artificial intelligence (AI) has experienced remarkable growth over the past decade, driven by breakthroughs in machine learning, natural language processing, and other advanced technologies. At the heart of this evolution is agentic AI, which can operate independently and handle complex tasks with minimal human intervention.

These agentic AI systems enable organizations to:

• Tackle complex tasks and perform tasks autonomously

• Execute decisions

• Automate routine processes, all without the need for constant human oversight.

As agentic AI continues to mature, its ability to operate independently and adapt to new challenges is setting the stage for a new era in artificial intelligence.

Generative AI vs. Agentic AI Capabilities

While both agentic AI systems and generative AI represent significant advancements in artificial intelligence, they serve distinct purposes.

Generative AI is designed for content creation— generating original content in response to user prompts—by leveraging large language models and deep learning techniques. This technology excels at generating personalized content and enhancing creative workflows.

In contrast, agentic AI operates independently, executing actions and making decisions without human intervention. Agentic AI systems use machine learning algorithms and natural language processing to analyze data, recognize patterns, and make data-driven decisions in real time. Agentic AI is particularly effective at analyzing and solving complex problems across various industries, enabling organizations to address challenging and multifaceted issues that would otherwise require significant human expertise.

For example, agentic AI can automate customer service inquiries by understanding user intent and resolving issues independently, while generative AI can craft tailored responses or marketing materials. By combining the strengths of both, organizations can deliver seamless customer experiences and automate complex business processes more effectively.

According to research from Gartner, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024. Gartner suggests this increase will mean 15% of day-to-day work decisions can be made autonomously.

What's the Difference Between Agentic AI and AI Agents? 

AI agents are designed to perform specific tasks within a controlled or predefined environment. These systems are rule-based, focusing on repetitive, well-defined tasks. Think of customer service bots or automated scheduling assistants. 

On the other hand, agentic AI refers to systems that act independently and exhibit autonomy in their decision-making process. These AI models can perceive their environment, analyze that data, make decisions, and adapt over time. For example, self-driving cars leverage agentic AI to analyze its surroundings to make safe and accurate driving decisions.

How Is Agentic AI Used? 

AI agency is still somewhat limited at the moment. However, companies are innovating and developing agentic AI technology towards greater flexibility and adaptability. Agentic AI is now being used in a variety of AI applications, from content creation and data analysis to predictive analytics and customer service, demonstrating its versatility and integration capabilities. We’ve already mentioned self-driving cars. Agentic AI can also be leveraged to:

How Does Agentic AI Work? 

Agentic AI models typically follow a four-step process for problem-solving:

1. Perceive. Gather and process data from relevant sources. 

2. Reason. Understand tasks and generate solutions, usually through a large language model (LLM.) 

3. Act. Execute those tasks by connecting with external systems through APIs. 

4. Learn. Improve through a continuous feedback loop to drive more intelligent performance and greater accuracy over time. 

AI Systems and Infrastructure

The success of any AI-powered application relies heavily on robust AI systems and infrastructure. For agentic AI systems to function effectively, they require a solid foundation that supports continuous learning, advanced natural language processing, and sophisticated knowledge representation. Leveraging cloud-based infrastructure and specialized models, such as large language models, allows AI agents to gather data, process information, and execute tasks efficiently. Seamless integration with external tools and content management systems further enhances the capabilities of these AI systems, enabling them to fit smoothly into existing business workflows. By supporting continuous learning and knowledge sharing, modern AI infrastructure empowers agentic AI to deliver intelligent, adaptive solutions that keep pace with evolving business needs.

Challenges with Agentic AI 

As with all AI implementations, the evolution to agentic AI presents its own challenges: 

• Security vulnerabilities. As AI models continue to evolve, security will be an ever present concern. Agentic AI can open the door to advanced cyberattacks, including smart malware, prompt injections, and malicious AI agents.

• Limited AI oversight. Especially since agentic AI systems can operate autonomously, a robust data governance framework is crucial for guiding AI models and ensuring responsible data management that aligns with regulatory compliance and ethical considerations. 

• Lack of AI-ready data. In order to perform autonomously, agentic AI needs to process a lot of data. Data is the foundation of all AI, including agentic AI.

To take advantage of agentic AI, data must be prepped for AI use, which includes removing data silos, integrating disparate datasets, or future-proofing your data strategy. According to a recent Google survey, only 44% of respondents were confident in their organization’s data quality.

Preparing for Agentic AI 

In 2025, research firm Forrester named agentic AI a top emerging technology for 2025. Analysts say that, “agentic AI is not just a step in the evolution of automation; it is a breakthrough capability that will become a competitive necessity.” 

But in order to truly get the benefits of agentic AI, you’ll need to start strategizing how you can prep your data, infrastructure, and teams for agentic AI.