Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
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“An AI agent can respond to your messages 24/7, when a person is still asleep at 2AM,” according to Nancy Xu, Chief Executive Officer of AI recruitment company Moonhub. What else autonomous AI agent can do?
This was the subject of a highly anticipated panel session – What Can AI Assistants Do? – at the World Economic Forum’s Annual Meeting of the New Champions in Dalian, China in June. One of the speakers of the forum was: Xi Kang – Assistant Professor, at Owen Graduate School of Management, Vanderbilt University.
For Kang, an AI agent or assistant is “any algorithm or models powered by AI or related technology that can help people make predictions about the future or make decisions, if it is approachable enough for laymen to interact with it, to get some insights from it.” In her view, this encompasses a wide range of AI development solutions created to assist users in navigating complex information and making informed choices based on accessible and user-friendly interactions.
So, let us try to find out the basics of custom AI agents, their key capabilities, and, most importantly, how to build them step by step. We’ll explore the fundamental concepts behind AI agents, diving deeper into their architecture and core functionalities. By the end, you’ll have a clear understanding of the agent development process, enabling you to create your own custom AI agent platform.
One of the easiest ways to understand AI Agents is to start with their well-known versions – AI Chatbots. You may find a lot of articles about this type of autonomous AI agent in our blog, however, in this one, we are going to remind you shortly about its key features.
So, the key features of an AI chatbot as an AI agent may include:
A chatbot could be a great example of an agent as custom AI Agents are usually developed to automate tasks such as answering customers, research, data analysis, and content creation.
Autonomous AI agents stand out due to their proactive nature and decision-making abilities. Unlike passive tools, these agents actively interact with their environment, making choices and executing actions to achieve their specified objectives. Their dynamic engagement ensures that they are not just reacting to inputs but are also strategically working towards their goals.
A necessary feature of AI agents is their ability to learn and adapt. Using modern technologies such as large language models, these agents continually update their performance based on previous interactions. When we talk about autonomous AI agent systems, multiple agents can play roles similar to members of a professional team. Obviously, autonomous agents are becoming game-changer tools in the new tech era.
By using an autonomous AI agent, you can develop custom generative AI solutions that seamlessly integrate into your projects, significantly saving time and enhancing productivity.
Moreover, the implementation of custom AI Agents allows you to accomplish more tasks efficiently and intelligently.
In the modern world of AI agent development, the architecture of agents plays a significant role in determining their behavior and capabilities. Understanding these architectures is key to understanding how AI agents operate, make decisions, and solve business problems.
So, let us determine three major types of agent architectures that form the foundation of AI agents: Reactive Agents, Deliberative Agents, and Hybrid Agents.
Reactive agents operate based on the current state of their environment, instantly reacting to changes without the use of internal models, like LLMs, or historical data. These agents are created to respond in real time, making them highly efficient in dynamic and unpredictable environments. For example, a reactive agent might be integrated into an automated customer support agent, which needs to adjust its path immediately due to the replies of the customer.
Technically, reactive agents rely on a set of condition-action rules, sometimes referred to as production rules. These rules are usually straightforward and understandable: “If condition A is true, then perform action B.” The simplicity of this architecture enables fast processing and execution, making reactive agents ideal for tasks that require immediate responses.
Deliberative agents, on the other hand, are the strategists of the custom AI agents. They use internal models to plan actions by considering the future consequences of their actions. This approach allows them to perform complex strategies and make informed decisions based on a comprehensive understanding of their environment.
Such an autonomous AI agent is equipped with a rich knowledge base and advanced reasoning technologies. A usual example of a deliberative agent is an autonomous vehicle, that is able to plan its route, anticipate traffic conditions, and make decisions based on a multitude of factors to ensure safe and efficient road travel.
Hybrid agents are the most powerful machines in terms of AI agent platforms, as they combine the architecture of both previous models. In other words, hybrid custom AI agents can respond immediately to environmental changes while also engaging in long-term planning based on modeled outcomes. Such agents can switch between reactive behavior and deliberative planning as the situation demands, making them exceptionally adaptable and robust.
From a technical perspective, hybrid agents are often powered by layered architectures. One of the most popular types of architecture is the three-layer architecture, consisting of a reactive layer for immediate response, a deliberative layer for planning, and a coordination layer to manage the interaction between the two. This structure enables agents to perform a wide range of tasks, from real-time responses in fast-paced environments to strategic planning in complex scenarios.
From the schema above, we may see that AI agent architecture is usually presented by the following key elements:
Overall, the architecture of AI agents shows their style and potential effectiveness in various use cases. As AI technologies continue to grow, these architectures will play an increasingly important role in developing intelligent systems capable of tackling a diverse array of challenges. Whether it’s for instantaneous reactions, strategic planning, or a blend of both, understanding these core architectures is essential for anyone interested in the field of artificial intelligence.
We are close to the super exciting part of our journey – answering the question of how to create AI agent or how exactly to build AI agent without coding or being a programmer.
So, the first step will be simple – you just need to visit our homepage and click on the “Free Trial” button.
As soon as you are redirected to our AI Agent Builder IONI, all you have to do is proceed with the straightforward registration steps. That’s it. If you registered successfully, you may sign in and will be redirected to the homepage automatically.
Your homepage shows you a graph of your customers’ interactions. Don’t worry if it is blank from the start – it will be filled as soon as they start to act with your agents.
Before creating multiple agents we suggest you test your already existing agent integrated into the platform – the AI chatbot. Only then we suggest to build AI agent from scratch.
For these, please go to the Knowledge Base page and fill it with the data you would like to be processed by the agent. You may use regular text, HTML, or a single URL data uploading option.
Now, we are ready to create AI agent ourselves. Please, move to the AI Agent page.
Note! There is already a pre-installed (default) agent for you. So, if you do not need to create multiple agents, you may just customize the existing one using the “Branding”, “Call to Action” and “Settings” tabs.
If you would like to build AI agent additionally, feel free to click the “Create AI Agent” button in the upper right corner.
Put the name of your other agent and click “Create”. That’s it! Now, you may test and use your other AI agent on other websites, business applications, and so on.
The world of AI technologies is unlimited but not all of them can be used to fine-tune and tailor AI agents. Customizing AI agents involves a variety of AI and ML frameworks, tools, and services, depending on business goals, such as building NLP chatbots, recommendation systems, computer vision models, or other AI agents.
Overall, these tools and technologies provide a great opportunity for professionals to customize AI agents to fit specific use cases, whether you’re working on AI chatbots, computer vision solutions, reinforcement learning, or other business apps.
The growth of AI agents is expected to speed up significantly in the coming years, driven by advancements in machine learning, natural language processing, and autonomous AI technologies.
AI agents will be more and more integrated into various industries, from customer service chatbots and AI assistants to complex computer vision systems in healthcare, fintech, and logistics. As these technologies evolve, AI agents are becoming more sophisticated, and capable of understanding and responding to human inputs in more natural and contextually relevant ways. This progress is also fueled by the growing availability of large datasets and more powerful computing resources, allowing AI agents to learn and adapt more effectively.
Moreover, as AI agents become more practically useful in everyday applications, their role in society is expected to expand significantly. They will not only assist with routine tasks but also provide personalized services, improve processes for decision-makers, and even collaborate with humans in creative industries like logo design, graphic illustrations, and so on.
This will likely lead to a greater reliance on AI in both professional and personal contexts, potentially transforming how we work, communicate, and interact with technology. So, we are moving to a new era – the period of AI employees and human-AI job cooperation.
