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There is no denying the exponential growth of AI (artificial intelligence) software, products, and solutions over the past few years. These tools have made waves in virtually every vertical, and are transforming the way that organizations get work done.
If you’re learning about building automated processes to increase efficiency, you’ve probably discovered the different types of AI like conversational, generative, and agentic. But the two that are most commonly misunderstood are Generative AI (GenAI) and Agentic AI.
While they do share similarities, they each serve different purposes. So let’s compare Agentic AI vs Generative AI to see what makes each of them unique, and which would be a better fit for you.
What is Generative AI?
Generative AI, which is perhaps the most commonly used AI technology today, is all about content creation. It harnesses advanced learning tools to analyze its training data, identifying structures and patterns present in its training data. Generative AI can create fresh content based on predictions from the patterns it has learned—you can think of generative AI (often abbreviated as simply GenAI) as a highly complex version of the autocomplete tool in a messaging app.
You’ve probably already been using generative AI applications in your day-to-day life. Some of the most popular GenAI tools include ChatGPT, Jasper AI, and Gemini by Google. Applications for GenAI tools go far beyond pattern-based generation, though; for example, using generative AI as an “interactive study buddy” in education, or as a brainstorming tool for content creators.
GenAI can generate content based on patterns and probabilities learned from human responses and feedback (though like with all AI, the accuracy of its output depends on the quality and relevance of the training data, as well as ongoing human validation and oversight). GenAI tools are often highly applicable in the digital transformation process, during which organizations integrate digital technology into every aspect of their operations. These tools help organizations streamline processes, enhance operational efficiency, and deliver improved customer experiences.
When it comes to IT, we often see generative AI tools used in tasks like ticketing and help desk assistance, code generation, process automation, and predictive analytics. Sounds pretty incredible, right? It is, and it certainly has its place within IT operations… but now Agentic AI is opening up a lot of new capabilities within the entire AI landscape.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems designed to operate autonomously, make decisions, take actions, and pursue complex goals with minimal human supervision. Agentic AI solutions therefore exhibit capabilities that set them apart from other AI solutions:
- Autonomy: Agentic AI can operate independently, making real-time decisions without constant human input.
- Goal-oriented behavior: These systems can define specific objectives and plan to achieve them rather than performing predefined tasks.
- Adaptability: Agentic AI can dynamically adjust its strategies based on changing environments and new data.
- Reasoning and decision-making: These systems can weigh options, anticipate outcomes, and change plans to respond to complex situations.
- Natural language understanding: Agentic AI can comprehend and follow complex instructions in natural language.
- Workflow optimization: It can efficiently execute and optimize multi-step processes.
When considering Agentic AI vs Generative AI, the main pro of using agentic AI is that it can act autonomously. Agentic AI systems are goal-oriented—they are designed with specific objectives in mind, which they then work toward achieving. They are capable of adapting to situations and learning from their experiences in real time. Other types of AI, on the other hand, cannot self-correct, plan towards goals, or take independent action.
With its ability to independently analyze situations and then choose actions that will further its defined goal, Agentic AI yields numerous potential use cases. While most Agentic AI programs are currently in their early stages, enthusiasts and researchers theorize that there could be applications for these models in AI robotics, customer service chatbots, task automation, and data analysis.
Agentic AI vs Generative AI
So, what is the difference between Agentic AI and Generative AI? Essentially, they serve different purposes: GenAI is designed for content generation, responding to prompts to create outputs like text and images, while agentic AI focuses on decision-making and autonomous action, capable of planning, executing tasks, and adapting to goals with minimal human input. Take a look at the table below to see how Generative AI and Agentic AI compare.
Comparing generative AI vs agentic AI
Generative AI | Agentic AI | |
Goals | Generate novel and creative content in various forms based on training data patterns | Complete complex, multi-step tasks to achieve defined goals without human intervention |
Applications | Generate content, create design and media assets, write code and scripts, simulate data analysis | Robotics, advanced customer support chatbots, data analysis, autonomous operations, task automation |
Examples | ChatGPT, DALL-E, Google’s Gemini, Jasper AI | Atera’s Action AI™ |
Responses | Can produce creative outputs including text, images, code, and other forms of data and solutions based on learned patterns | Can take autonomous actions, make independent decisions, and determine strategies aligned with the program’s goal(s) |
Technology | Deep learning networks allow tools to generate text and images | Real-time decision-making frameworks like reinforcement learning and memory-based reasoning enable tools to analyze challenges, develop strategies, and execute tasks |
User interaction | Does not engage directly, but rather adds to user experience by creating useful, fresh content | Dynamically engages with the environment in order to further the pre-assigned goals |
Impact | Great for enhancing productivity and innovation by automating content creation, streamlining workflows, and supporting IT and other industries with data-driven insights | Great for automating processes and boosting productivity with intelligent, adaptable actions while minimizing human intervention |
So, Agentic AI vs Generative AI – which should you use? Well, at the end of the day, it boils down to your organizational needs.
For organizations that are looking to develop autonomous processes that run in the background without the need for human intervention, you’ll need Agentic AI. And for those who are looking to use AI to make input-based processes more productive, you’ll need to use Generative AI.
But they’re not mutually exclusive. In most cases, a combination of the two will give you the best results. While Agentic AI programs are being developed in their early stages in many different verticals (Google is set to release its Agentic AI update for general use in early 2025, and Atera is already offering its AI Copilot, with Autopilot to join soon), Generative AI bridges the gap to help create a more productive and efficient workplace. Experts predict that Generative and Agentive AI will be used in tandem for a holistic AI transformation.
Agentic AI vs Generative AI in IT
As we’ve covered, Generative AI and Agentic AI each serve a distinct purpose and aren’t necessarily a “one or the other” choice. They’re both transformative aspects of a technology that you can use to optimize your organizational efficiency.
They both share the core purpose of enhancing efficiency and innovation in the world of information technology. However, the difference between Agentic AI and Generative AI boils down to autonomy. Agentic AI programs are focused on autonomous, goal-driven actions. On the other hand, generative AI programs offer specialized creation of outputs that are based on learned patterns.
When it comes to applying both Agentic AI and Generative AI in the IT field, experts agree that these programs will likely be used in combination to provide comprehensive AI-driven tools. For example, Agentic AI programs can leverage the power of GenAI to create content or contextual data for a specific task.
Ultimately, combining the power of Agentic AI and Generative AI for IT use cases will yield more flexible and robust AI capabilities. These solutions will be able to leverage various models to perceive, calculate, and evaluate a situation—and then respond accordingly.
Elevate your IT with Agentic AI
If you’re looking to run your organization at peak efficiency, you need to take advantage of utilizing AI to systemize and automate time- and labor-intensive processes.
Atera is the first IT management platform powered by Action AITM, an Agentic AI technology. The platform combines Remote Monitoring & Management, IT Automation, patch management, scripting, Helpdesk & Ticketing, reporting, and dozens of integrations, all in a single platform—enabling unprecedented levels of efficiency for 12K+ IT teams and MSPs globally.
Atera offers two main types of AI agents, Autopilot and AI Copilot, resulting in a major impact on how the entire organization flows.
Atera’s AI Autopilot enables end users to self-troubleshoot repetitive fixes and tasks, freeing IT technicians up to handle more complex IT issues and more strategic initiatives. This makes the support process more efficient and enables IT professionals to concentrate on critical tasks like security and addressing technical debt. As a result, response times are significantly reduced (often the top concern for IT departments), and organizational efficiency sees a tenfold increase.
AI Copilot is integrated into tools like helpdesk and ticketing, enabling knowledge base article creation, remote session summarization, command-line generation, script writing, ticket summarization, real-time troubleshooting, process automation, and so much more—allowing IT professionals to save an average of 11-13 hours every week.
Want to explore more of Atera’s innovative IT solutions? You can test out our platform at no cost with a 30-day free trial, no credit card required.
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