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 AI? 

So what is agentic AI, and how is it different from generative AI? 

Generative AI, like ChatGPT or DALL-E, is artificial intelligence that can create original content—such as text, images, video, audio, or software code—in response to a user’s prompt or request. 

In contrast, agentic AI is focused on making decisions and doesn’t solely rely on human prompts nor require direct human oversight. Essentially, Generative AI requires prompting, but agentic AI has autonomy to make decisions and take actions on its own. 

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. We’ve already mentioned self-driving cars. Agentic AI can also be leveraged to: 

1. Automate workflows

    

    Agentic AI can help manage business processes and handle complex tasks independently without needing human intervention. For example, a logistics company can use agentic AI to automatically adjust delivery routes based on real-time traffic conditions. 

2. Serve customers more effectively 

    

   AI agents or chatbots can help customers with predefined answers, but this limitation often forces customers to escalate their issues to a human representative. In contrast, an agentic AI model can better understand the customer’s emotion and intent to suggest steps to resolve their issue, freeing representatives to handle more strategic tasks.

3. Make informed decisions

    

   Because agentic AI models are able to process vast amounts of data, they can quickly recognize patterns, identify outliers, and make decisions. For example, healthcare providers can leverage agentic AI to monitor a patient's habits, medication, and history to identify potential health issues.

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. 

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.

So how can you make your data ready to fuel agentic AI? Read this blog for the keys to unlocking AI success.

Preparing for Agentic AI 

Forrester notes that 90% of businesses expect agentic AI to impact their competitive advantage within the next five years significantly. A Deloitte survey further reinforces this point, indicating that companies using AI for strategic decision-making outperform their peers in revenue growth and innovation, with an average market share increase of 15%.

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. The potential for agentic AI has barely been touched, but if your organization isn’t equipped for these models, you won’t reap the benefits.