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To start the definition of AI agents from a very basic definition, think about AI agent as your assistant or as a software, if you ask your assistant to write email. If you ask to write an e-mail, it will write to you with e. It doesn’t play music or do nothing else. The first thing that can be said about these agents is that they are smart. They know what goals they get and what actions should take to do the job. The purpose of AI agents is currently AI agents can also make a wide range of tasks. Some of these tasks. Browse the Internet Write and Drive Codebook, which meets pay, analyzes and keywords, etc. We see new progress of artificial intellect every day, so AI agent cannot be the current limitations on the list of things. Every new progress of artificial intelligence, especially the technology of agent technology brings us closer to AGI (artificial general intelligence). Preference for AI’s interaction, 51% of consumers prefer bots for immediate service to human agents, reflecting dependence on AI for quick support. In addition, 68% of Chatbots thinks that the need for skilled human agents should comply with the need for improved investments in AI and future expectations during the year, and 58% expect their conversations 2024. To be more complicated than the date, indicating the trust of AI agents to improve customer experience. Six types of AI drugs 1. Simple reflex agents this is more like the coding of agent behavior. It works on condition-action management, which means that it operates after viewing the current state. Agents cannot plan their next step or learn and improve their reasoning by learning from past experience. Although it is very easy to implement and run it, it has changed very frail. Because this category of agents is not equipped with memory, they should not keep any conditions, they have very limited or any intelligence. One copy of the public type agents is regulated by Chatbots, which has previously planned answers to the user’s inquiries. 2. Model Reflex Agents AI AI have autonomous valid capabilities for smart decisions. These agents work in four degrees. Judgment. Here, agents will act the current state of the situation. Model. This step gives itself the view after the current environment has experienced. Reason based on the above model now determines how to act prior to acting. Action: Here the agent acts. The example of Model Base Reflex agents is AWS BEDROCK, which uses various fundamental models to make decisions based on user tasks. Although these models are quick and better through decision making, it is expensive. 3. The target agent-based agents are different from the two above, as they retain the information of their environment to achieve special goals. They have three parameters that they take into account when working. The current condition needs the ultimate goal of gaining a number of actions to achieve the goal. These agents are very effective when they are deployed to achieve a special goal, but it can fail a difficult task. 4. The agents of utility agents are quite advanced, as they can provide utilities for different ways needed to take scenarios. Consider a script when there is an agent designed to conduct research. However, a certain task has two options. Search the web or pass the vector store for a subgroup fill. In this scenario, this agent can add auxiliary programs to these individual roads, then determine which one should take to complete the special task. The main advantage of these agents is that they can be well done in different decisions in different scenarios. It also learns from the experience of the past and, consequently, adapts its decision-making strategy. 5. The agents studying agents are the types of AI drugs that can learn from the interaction of the past and can improve their performance over time. These AI drugs learn from complex data forms and can also have feedback from the people of the loop to adjust accordingly. 6. Hierarchical AI agents of hierarchical agents are similar to how things are performed in the organization. Lower -Level Hierarchy Agents carry out tasks, and aging agents above them control them. This type of AI is very good to prioritize different tasks, instructing a legal agent to the correct package of tasks. Use AI Agents Cases 1. Individual Virtual Assistants This is a very common case that includes AI agents. They can help us with different tasks such as reminding us of important events, planning our day, email and planning meetings. 2. In the field of health AI, AI health agents may have a significant impact. This can make everything possible, from basic tasks such as remarkable tasks to help their medical surveys such as drug detection. Many pharmaceutical businesses, such as Gilead sciences and others, have already seen the potential of AI agents. Before the survey had been previously years old, they accelerated the whole process, which was possible during months or even days. 3. AI’s financial agencies can use financial institutions as a financial analyst to help them discover fraud using previous data. They can also use agents to build a client’s post-election chat, which respond faster and more accurately to the users’ questions. In addition, autonomous financial analysis agents can analyze market trends, assess risks and provide ideas for investment decisions that improve the general financial strategy. 4. The researchers and researchers of education and researchers can help agents conduct research on the entire Internet in natural language. This reduced research articles to manually review the time required and perform the best content of any research. Work-intensive workflows will turn into educational institutions into educational institutions. Importantly differences between typical language models and AI agents, based on real-time feedback, and the lack of limited memory and limited lack of limited measurement, use both short-term and long-term memory, interactions that provide. Personalized reactions. Memory can also be spread on the tools of many agents. The foreground. Knowledge with fixed teaching is dynamically adapted to new information and real-time knowledge accuracy, relying on potential arguments, which are based on the output and system autonomous agents of the system’s autonomous customer agents. Immediate support to customer data effectively and personalize interactions. The best open source agents 1. Completion This is available to one of the first and top agents of AI agents. This is first it first creates a number of subgroups. Then it passes every sub-problem to do the job. This process can even be subjected to a subsequent subgroup of the task based on the complexity of the task. It is the agent who divides the complex task into sub-performance and is repeated to complete their work. 2. Andre This is one of the remarkable releases in the field of autonomous drugs. This allows you to create a multi-agative discussion framework within your application for the accuracy of LLMs and better deviations. For example, you have to deal with your problem of questioning your structural database. With Autogens, you can switch to the original keyword results in the middle of different agents in the middle before handing over exports to the user. Each agent has a defined problem and a role given to it. If the agent considers the answers insufficient for any step, it sends them to the previous agent for reversal. AI agents levels 1. Tools (perception + action) 2. Reason and decision making LLM -angent figure -Lele AI agency reference. –
