AI, or Artificial Intelligence, is everywhere around us. From smartphones, chatbots, search engines, and voice assistants, you name it. However, are we utilising the full potential of artificial intelligence in every sector? Most customer service sectors say otherwise.
The problem is that customers want faster, smarter and personalized support solutions, and many companies are relying on legacy systems and methods which are unable to keep up with the massive demands. This is where an AI agent should be implemented. But what is an AI agent? This is exactly what we walk you through, along with their benefits, in this blog; let’s get started.
What is agent in AI? Let’s cut to the chase: AI agents are software systems that utilize artificial intelligence to process and complete tasks without human intervention. Compared to regular AI chatbots that have scripted rules and responses, AI agents understand the context of the task and make well-informed decisions by processing vast amounts of data.
Some of you may have been confused between what is agentic AI and what is AI agent. Think of AI agents as individual pieces of AI designed to perform particular tasks. They are programmed to operate within defined parameters and can automate repetitive tasks. This level of automation has also proven beneficial in various fields, including automating SEO, where AI-driven agents streamline processes like keyword analysis, content optimization, and performance tracking. Agentic AI, on the other hand, is a much broader concept that describes AI systems capable of autonomous decision-making and action without human intervention.
If these software tools can accomplish human tasks quickly, are they designed to replace humans in customer support? The answer would be no for the time being because there will be various situations that will require emotional intelligence and nuanced judgment, which only human agents can handle. So, to answer the question, just think of them as assistants that work with humans and improve efficiency.
Now that you know what is an AI agent, let’s see how they work. These systems integrate technologies such as natural language processing (NLP), machine learning (ML), large language models (LLMs), and data analysis into a seamless process and operate autonomously. They can adapt to changing business environments. We break down the whole process into 6 simple steps:
1) Objective setting: The initial process is setting the goals that the system should achieve, which is done by human programmers and developers. These can consist of both simple and complex goals.
2) Data Gathering: Whatever query the AI agent is presented with, the tool perceives it and gathers real-time data to give an accurate output or guide users to another section.
3) Understanding the intent and processing: Once perceiving its environment, AI agents utilize natural language processing to analyze the information received and understand the user intent.
4) Deciding the best course of action: Once the AI agent has gathered the required data, the next step is implementing its decision-making capabilities. It makes a best course of action based on its trained rules and learned experiences.
5) Executing the selected action: Once the AI agent makes a best course of action, this means that it provides a valid solution to the user. This can include task automation, adjusting a specific process, etc.
6) Continuous learning: This is an ongoing process of AI agents. Once the systems execute an action, they assess whether it was resolved or if there were any
errors, etc. Through this process, the software system continuously learns and adapts to evolving user requirements.
So far, so interesting. After covering what is agent in AI in the above sections, the next part is to learn the types of AI agents. After all, you must know the various types so that you know which can be used to solve problems and improve efficiency based on different scenarios. AI agents are divided into two categories: based on user interaction and the number of agents. Other factors considered include their capabilities, type of environment and roles programmed into them.
Based on interaction: Depending on the scenario, some agents might engage in direct conversation, while others perform tasks without user input directly in the background.
These are the ones that interact with customers and assist in various tasks such as customer service, education, scientific discovery, etc. and provide personalised support.
Also known as autonomous background processes, these agents work in the backend without interacting with users. Their works include routine task automation, data analysis, and identifying and optimizing processes for efficiency.
Based on the number of agents: It’s obvious from the name itself. Some workflows may not need multiple agents to achieve a specific goal, but sometimes, it might be the opposite. Let’s know what they all are:
These are usually agents that operate independently. External tools and resources are leveraged to achieve the required results. A single agent is best suited for tasks that don’t require collaborating with other AI agents.
In this scenario, multiple AI agents work together to achieve a common objective. In this approach, the system utilizes the varying strengths of individual agents to accomplish complex tasks.
Learning what is AI agent and its various types is just one part of the process. AI agents can better handle a ton of user queries and drastically improve business operational workflows. This, in turn, increases user satisfaction and minimizes work errors and delays. If you are working in the tech sector, understanding the benefits of AI agents gives you better exposure.
As said above, AI agents can process enormous amounts of user data in real time and can quickly identify any sort of problems, performance issues, customer complaints, etc. Apart from finding out the issues, it quickly suggests effective solutions that allow concerned people to take action.
One of the key perks of AI Agents is that they can effectively model human-like social behaviors. Some of this includes forming relationships, sharing information, etc. Another aspect is the complex social interactions that are generated naturally by interacting with single agents.
Working in the tech sector isn’t easy as you’ll have to deal with endless types of data such as bug reports, customer feedback, previous code iterations, etc. And the worst part? Find the information you need from them quickly. Manually, this ain’t possible however, AI agents get the job done by cutting down the time and resources. It identifies top user complaints, bug fixes, and a lot more. This, in turn, accelerates workflows.
As explained in the intro section, AI agents are designed to not replace humans but rather work with them and improve workflows with ease. With these systems offering innovative capabilities, we should expect a futuristic scenario where AI agents work autonomously on the user’s behalf. AI based test automation tools will definitely be adopted by almost all modern organizations to support their customers, build efficient products, and offer higher user satisfaction. Do you still wish to learn more about what is agentic AI or AI agent is in detail? We recommend you get in touch with us for more details.
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