Image by Author | IdeogramAgentic AI has recently become the hottest topic in AI implementation. If you follow AI information on social media, you are likely to see posts about agentic AI. Its popularity is increasing because many believe that agentic AI will become the next big thing in the AI field, as it can act independently.
Given the popularity of agentic AI, it’s no wonder that many people are jumping into the hype and learning more about it. However, there are a few things we need to understand before jumping into the agentic AI bandwagon.
In this article, we will discuss five key points about agentic AI. Let’s get into it.
1. Agentic AI Definition
Understanding the concept of agentic AI requires understanding its definition. If we try to define them, agentic AI could refer to an AI system that possesses agency. The agency itself is the ability to act independently with minimal human supervision to achieve an objective. It differs from a simple automation or any rule-based program, as an agentic AI system is capable of developing its actions to solve problems rather than sticking to a pre-defined rule. Essentially, agentic AI is more sophisticated than other AI systems because it can mimic the human decision-making process.
Agentic AI works by understanding its environment, reasoning to develop plans, executing the plans, and learns from the output. Under the hood, agentic AI often integrates various machine learning techniques, including reinforcement learning, deep learning, and natural language processing, among others. By combining all the advanced methods, agentic AI can tackle more dynamic and complicated workflows.
2. How Agentic AI Differs from Other AI
We have understood that agentic AI is an autonomous AI system, but let’s explore further why we separate it from traditional AI. The key differences between agentic AI and other traditional AI systems lie in their proactiveness. Traditional AI often focuses on rules that have been previously defined by users and requires some human input whenever it needs to execute tasks. In contrast, agentic AI adapts to the environment and formulates its plan to achieve objectives. Often, traditional AI is used for repetitive and predictable tasks that cannot deviate from their scripts, while agentic AI can handle any surprises by evaluating the conditions.
Agentic AI differs from generative AI, despite their relationship. You may understand that generative AI models, such as ChatGPT or Stable Diffusion, enable the generation of content, including text and images. However, generative AI can only produce content when prompted and cannot create any content autonomously. In contrast, agentic AI utilises the output from generative AI by planning and executing more complex actions that incorporate the output.
In summary, agentic AI is more proactive and capable of responding to its environment to achieve its objectives compared to other AI systems.
3. Agentic AI Technology
Agentic AI is not an outdated technology; it is an emerging field, thanks to advancements in the reasoning of generative AI models. As an evolving field, we are still in the initial phase of understanding how the technology can develop into something more significant. Many experiments have been conducted in agentic AI over the past few years, including the open-source frameworks of AutoGPT and BabyAGI, which have demonstrated the utility of LLMs for planning and executing multi-step tasks with minimal human intervention. This new technology generates hype, but few companies have implemented agentic AI yet, as the technology is not yet ready to support a stable, autonomous AI system integrated with their current systems. This means that the technology is still in a relatively early stage of adoption.
Despite being in an early adoption phase, agentic AI technology has demonstrated numerous real-world applications that are crucial in various business contexts. Many tech and business leaders are experimenting with agentic AI systems to determine if the technology is suitable for company tasks such as software development support, customer service automation, and more. One of the most famous examples of agentic AI is the self-driving vehicle, which relies on the AI agents to understand its surroundings and execute driving decisions.
Overall, agentic AI technology is already here, although it is still in its early stages. The adoption will still take time, but many big companies are investing in the technology to improve its effectiveness in real-world situations.
4. Agentic AI Implications
With its autonomous properties, agentic AI has the potential to transform how we work and live. In today’s technology, many tasks and business processes are mostly static and not adaptive to the environment, which already leads to significant productivity gains. Imagine if automation is now capable of making more complex decisions and working all day for routine tasks; this will lead to even greater efficiency and improvement in various business departments. The system is freeing employees from performing repetitive tasks, allowing them to focus more on important strategic tasks.
Of course, agentic AI also presents considerations and challenges when it is properly implemented. A discussion regarding agentic AI on its reliability in decision-making is something that needs to happen. When we hand over decision-making to machines, we must ensure that the decisions align with business needs and adhere to ethical guidelines. The need for reliability is also related to the concern of transparency, as an agentic AI system needs to explain its reasoning for arriving at the decision it made. Transparency is what makes people trust the system, but sometimes, agentic AI can be too complex to explain its decision-making. Lastly, the safety of agentic AI is a challenge that needs to be considered, as autonomous agents can connect to various sensitive tools and data, which could be compromised without proper safeguards to control them. The consideration and challenges become an essential part of the discussion as part of the agentic AI implications if we want to rely on the autonomous system.
Agentic AI have the potential to transform how we work. Still, a few key considerations, such as reliability, transparency, and safety, must be present if we want to have a reliable agentic AI system.
5. Common Misconceptions About Agentic AI
As agentic AI trends grew, many misconceptions arose regarding the technology. Let’s address them so we can better understand the concept.
One misconception people have regarding agentic AI is that it is seen as a fancy chatbot. It’s easy to see that conversational AI powered by the agentic AI system is similar to the usual chatbots we have. In reality, agentic AI are fundamentally different from the usual chatbot. For example, both chatbots and agentic AI can hold a conversation with you, but agentic AI can perform tasks we ask for using natural language and complete them without step-by-step instructions, whereas a standard chatbot cannot independently perform tasks.
Another misconception is that agentic AI will replace human workers overnight. With so much hype about how agentic AI can perform tasks autonomously, many think that the system will replace human jobs. However, most agentic AI system today works as assistant tools rather than fully autonomous replacements. Rather than replacing human work, agentic AI is much better at augmenting human work, such as handling routine or data-intensive tasks, so that humans can focus on much higher-level work.
Lastly, the misconception about agentic AI is that it cannot be controlled once the system is executing. Many thought that agentic AI is a system that will do whatever it wants once in production. However, the developer will build guardrails and limit the system once it is in production to ensure the system is safe. We need to think of agentic AI as a tool that we can still control, even if it’s acting on our behalf.
Conclusion
Agentic AI is a popular technology with considerable hype surrounding it. Although useful, we need to understand them before implementing them due to the hype.
In this article, we explore five different things you need to know about agentic AI. I hope this has helped!
Cornellius Yudha Wijaya is a data science assistant manager and data writer. While working full-time at Allianz Indonesia, he loves to share Python and data tips via social media and writing media. Cornellius writes on a variety of AI and machine learning topics.
