What are AI agents? 

An artificial intelligence (AI) agent is a software or system that can define its workflow and use the tools at its disposal to carry out tasks on behalf of a user or another system. 

Beyond natural language processing, artificial intelligence (AI) agents may perform a wide range of tasks, such as making decisions, solving problems, interacting with the outside world, and carrying out activities. From software design and IT automation to code-generation tools and conversational assistants, these agents can be used in a variety of applications to accomplish complicated tasks in a range of organizational scenarios. Large language models (LLMs) employ sophisticated natural language processing techniques to understand and react to user inputs in a step-by-step manner, as well as to decide when to use external tools. 

How AI agents work 

Large language models (LLMs) are the foundation of AI agents. Because of this, AI agents are frequently called LLM agents. Conventional LLMs are constrained by knowledge and reasoning constraints and generate their answers based on the training data. Agentic technology, on the other hand, makes use of tool calling on the backend to get the most recent information, streamline processes, and independently generate subtasks to accomplish complicated objectives. Over time, the autonomous agent gains the ability to adjust to user expectations. A customized experience and thorough responses are promoted by the agent's capacity to remember previous exchanges and anticipate future actions. This tool calling expands the potential uses of these AI systems in the actual world and can be accomplished without the need for human intervention. These three steps make up the strategy AI agents employ to accomplish user-specified goals: 

Goal initialization and planning 

Even while AI agents make decisions on their own, they still need human-defined objectives and settings. Three primary factors impact the behaviour of autonomous agents. 

• the group of programmers who create and train the agentic artificial intelligence system. 

• the group that gives the user access to the agent and deploys it. 

• The user specifies the available tools and gives the AI agent particular tasks to do. 

The AI agent then carries out job breakdown to enhance performance based on the user's objectives and the tools at its disposal.3. To achieve the intricate goal, the agent basically develops a plan of distinct jobs and subtasks. Planning is not a crucial step for easy activities. Rather than planning its next move, an agent might iteratively evaluate and refine its replies. 

Reasoning using the resources at hand

The information that AI agents see informs their actions. AI agents frequently lack the comprehensive knowledge base required to handle every subtask inside a complex goal. AI agents employ the resources at their disposal to address this. Web searches, APIs, external data sets, and even other agents can be used as these tools. The agent can update its knowledge base once the missing data has been recovered via these tools. This implies that the agent self-corrects and reevaluates its strategy at every stage. Consider a user organizing their trip to better understand this procedure. An AI agent is tasked by the user to forecast which week of the following year would probably have the finest weather for their Greek surfing trip. The agent collects data from an external database that contains daily weather reports for Greece over the previous few years because the LLM model at its heart is not an expert in weather patterns. 

The next subtask is formed since, even with this new information, the agent is still unable to identify the best weather conditions for surfing. The agent interacts with an outside agent that specializes in surfing for this subtask. Suppose that while doing so, the agent discovers that the ideal surfing circumstances are high tides and sunny weather with minimal to no rain. Now, the agent may find patterns by combining the knowledge it has gained from its tools. It may forecast which week in Greece will probably have high tides, sunny skies, and little probability of rain the next year. The user is then shown these results. AI agents are more versatile than conventional AI models because of this information exchange between tools. Feedback from other agents may also be used if they were employed to achieve the aim. The time that human users spend giving instructions can be reduced with the help of multi-agent feedback. To better match the outcomes with the desired outcome, users can also offer feedback while the agent performs its actions and internal logic. Iterative refinement is the term used to describe how feedback mechanisms enhance the AI agent's accuracy and reasoning.AI agents can also keep information about past challenges' solutions in a knowledge base to prevent making the same mistakes again. 

Learning and reflection 

To increase the precision of their reactions, AI agents make use of feedback mechanisms like human-in-the-loop (HITL) and other AI agents. To illustrate this, let's go back to our earlier surfing scenario. Following the formulation of its response to the user, the agent retains the knowledge it has gained and the user's input to enhance performance and adapt to the user's preferences for upcoming objectives. Feedback from other agents may also be used if they were employed to achieve the aim. The time that human users spend giving instructions can be reduced with the help of multi-agent feedback. To better match the outcomes with the desired outcome, users can also offer feedback while the agent performs its actions and internal logic. The process by which feedback mechanisms improve the AI agent's precision and reasoning is known as iterative refining.3. To avoid repeating their mistakes, AI agents can also store information about previous problems and their solutions in a knowledge base.