Agentic and Generative AI: Differences and Impact on Organizational Growth

Generative artificial intelligence (GenAI) went mainstream in 2022 with the launch of ChatGPT. Now, tech companies are turning their attention toward the next big advancement: agentic AI. Within the next few years, generative AI and agentic AI may coexist in the professional world, synthesizing information and streamlining operations more efficiently than humans can. These technologies could spur smarter cybersecurity initiatives and better organizational growth — provided that leaders know how and when to use each one.

While widespread agentic AI is not quite here yet, the time to start learning about it is now. GenAI can only respond to human queries, but agentic AI could act autonomously. Used responsibly, agentic AI could help organizations generate more innovative ideas and provide better customer service. Used irresponsibly, the technology could empower threat actors to create and spread devastating malware. To understand what agentic AI could do in the future, let’s compare it to what GenAI can do right now.

Key differences between agentic AI and generative AI

Although GenAI and agentic AI are fundamentally different technologies, they do share a common root. “Artificial intelligence” describes any computer algorithm that mimics human behavior. This could be as simple as sorting names in a spreadsheet or as complex as defeating a chess Grandmaster. At its core, every AI tool works by recognizing patterns and making “decisions” based on probable outcomes. Using a process called machine learning (ML), many AI systems can also “learn” to correct their mistakes over time.

What is generative AI? It’s a tool that can create novel text, images, videos, or other media in response to user prompts. Large language models (LLMs), such as ChatGPT, are prominent examples of GenAI. By analyzing huge quantities of training data, GenAI tools can reply to natural-language queries with responses that are usually both coherent and correct.

While GenAI can be a useful tool for ideation and analysis, it has a few notable limitations. First and foremost, the quality of a GenAI’s responses is directly proportional to the quality of its training data. GenAI tools cannot evaluate either the questions they receive or the answers they give. They formulate the most probable response, not necessarily the right one. Furthermore, GenAI systems cannot act on their own. They can carry out specific instructions that humans give them, but they can’t think, reason, or be proactive.

What is agentic AI? It’s a developing technology that aims to create autonomous, rational computer programs. These AIs would be independent entities (or agents) able to act of their own volition. While Agentic AIs would still study large sets of training data, they could also draw upon that data to reach completely new conclusions. They would be able to follow general assignment parameters rather than specific step-by-step instructions.

At present, agentic AI’s largest drawback is that it’s not ready for everyday commercial use. The technology is mostly experimental right now, and even simple tasks cost thousands of dollars to perform. If and when agentic AI becomes widely available, it could also raise some thorny legal and ethical dilemmas. An autonomous AI system could theoretically cause harm, especially if doing so were the most efficient way to solve a problem. Who is ultimately responsible for an agentic AI’s behavior? Can developers force human values onto synthetic systems? These science-fiction staples could become real-world questions sooner rather than later.

Agentic AI, generative AI, and organizational growth

If organizational growth is on your agenda in the immediate future, you’ve probably already integrated GenAI tools into your workflows. On the other hand, agentic AI is still in its early stages, so you may not yet know the best way to implement it. Using GenAI to streamline workflows and automate rote tasks can help organizations save time and money by freeing up employees for more interesting, demanding work. Because AI tools work quickly, they can also help organizations scale. While it might take a human being a few days to analyze thousands of documents, for example, a specialized AI tool could do so in a few hours.

Still, if agentic AI seems like it might be useful for your organization, now is a good time to start investigating the possibilities. In early 2025, Google provided an example of how agentic AI could generate detailed fantasy worlds with limited prompts. Imagine what that technology might look like in other fields, such as customer service. Right now, AI chatbots can respond to customer queries, but only with whatever information is in their training data. An agentic AI chatbot could analyze a customer’s issue, reason through it, and propose a completely new solution. It’s easy to envision similar tools for finance, healthcare, manufacturing, and any other industry that thrives on data-driven decisions.

Right now, organizations don’t have to choose between GenAI and agentic AI, simply because the latter isn’t usually an option. In the future, employees may want to use GenAI to streamline simple tasks and gather information. Agentic AI might be better for assignments where thousands of disparate data points could inform dozens of branching decisions.

Best security practices for agentic AI and generative AI

Over the past few years, AI tools have empowered both security professionals and threat actors. As such, cybersecurity must be a consideration in any AI-powered organizational growth strategy. GenAI has already had a major influence on modern security practices, while agentic AI could make an impact in the near future. Organizational leaders will need to keep a close eye on how these technologies evolve, as well as how relevant government and regulatory frameworks adapt.

Cybersecurity for generative AI

GenAI has made life easier for threat actors, particularly when it comes to phishing and other forms of social engineering. Shady tools such as FraudGPT, which lack ethical guardrails, help them craft convincing social engineering campaigns and spread malware. Even legitimate LLMs can aid in spear-phishing or whaling (aka CEO fraud or executive impersonation) attempts by combing through public posts from a target’s friend or coworker and mimicking their writing style.

However, cybersecurity companies have also created GenAI tools to help IT administrators. AI algorithms can analyze network activity for unusual access patterns. LLMs can analyze thousands of cybersecurity data points, while chatbots can present the results in clear, natural language. The arms race between threat actors and cybersecurity professionals will probably continue into the foreseeable future.

Cybersecurity for agentic AI

Right now, most cybersecurity concerns for agentic AI are theoretical. There’s no evidence that any threat actor has tried to create or release a malicious agentic AI in the wild, though a proof of concept for one does exist. Still, threat actors successfully circumvented the ethical guidelines on GenAI tools. There’s every reason to believe they could eventually do so with agentic AI. If and when that happens, the cybersecurity implications could be significant.

Agentic AIs could theoretically carry out sophisticated spear-phishing campaigns with minimal prompting, perhaps even creating phony websites, deepfaked voice and video calls, or customized malware packages to target specific individuals. Shutting down these AIs might be nearly impossible if they replicate and spread in the cloud.

Since malicious agentic AIs don’t appear to exist yet, it’s too early to develop ironclad countermeasures for them. For now, the best approach is to train or refresh staff members on common social engineering tactics and how to avoid them. Even with sophisticated delivery techniques, agentic AIs will probably still rely on familiar tricks such as copycat websites, spoofed email addresses, and exploiting overly permissive app settings. Multi-factor authentication (MFA), zero-trust principles, and fully patched software can prevent attacks from both human and AI attackers.

Enhance your organization’s cybersecurity with AI

While agentic AI isn’t widely available yet, generative AI can help power organizational growth today. Lookout uses AI and ML to detect threats, evaluate apps, and identify device risks in real time. These tools can also prevent phishing attempts by analyzing incoming messages and identifying misleading websites. Book a demo with us today to see how AI can help safeguard your data, your staff, and your entire organization.