From answering queries to managing tasks independently with AI Agents, we have now entered the era where AI agents plan tasks, take actions, and make decisions. However, what is an AI Agent? In simple terms, it is an advanced artificial intelligence system that works on its own to achieve human-defined goals. If you want to learn more about what an AI Agent is in detail and understand its workflow, real use cases, benefits, and limitations, then this guide is for you.
What is an AI Agent? An AI agent is an artificial intelligence system that can complete user-defined tasks independently. Using LLMs for natural language processing, the AI Agent perceives the information, plans and reasons through the task, makes decisions, uses tools, and takes actions to achieve the set objective.
The AI Agent demonstrates a certain level of autonomy in learning, adapting, and decision-making. Simply put, they connect LLMs with external tools and databases to handle tasks with minimal human supervision. They decide the best approach to complete tasks internally and use suitable tools to do so. 
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Understanding the AI Agent Workflow With Real Examples Now that we understand what an AI Agent is, the next thing is to learn how it works. This section explains the common steps an AI Agent takes to complete tasks with examples. Initiate the Task: To start, AI Agents need a specified objective set by the user. After receiving the instructions, the AI Agent starts working on the task under predefined conditions and rules. Step 1: Gather Information The first step an AI Agent takes when allotted a task is to perceive information from its environment. It collects data from multiple sources such as user interactions, sensors, and APIs. Let’s take an example of a Job Recruitment Agent. Suppose the Agent is given a task: Find a suitable candidate for a mid-senior Social Media Manager Role. Here, first the agent will collect all the needed information, experience and skills required, job location, salary range, previous hiring information, and study the job description. Step 2: Plan the Task After collecting the data, the AI Agent decides what to do with the gathered information and finds the best action to complete the task. It reasons through the task and reassesses possibilities to improve its plan. It breaks down the given task into smaller subtasks to complete it.
Again using the previous example, at this stage, the AI Agent will plan the best approach to find the most suitable candidate. It will decide the subtasks it needs to perform, such as:
Post the job on different job search platforms.
Collect the candidates' resumes.
Filter them through the required experience and skills.
Compare them against the requirements.
Rank the top candidates.
Schedule the interviews.
Post the jobs by using the tools required to access the job portal.
Use ATS to filter candidates.
Analyze the resumes and compare them against the requirements.
Build a list of the top candidates.
Use Email API and access candidates' information from the data to send emails.
Access the calendar to schedule interviews.
After completing the task, the AI Agent evaluates the result and reflects on it to improve its performance. It also works on the human feedback and adds the mastered data to its knowledge base to remember context for future actions.
At this stage, the job recruiter agent reflects on the results and analyzes whether it found at least 3-4 best candidates for the role.
Reasoning and Planning:
After receiving an objective, AI Agents break it down into smaller tasks and plan how to achieve the task. They use reasoning techniques to analyze the available information and find the best approach to achieve the goal.
Decision Making:
AI Agents have the ability to make decisions, they decide how to manage a complex task, find the best possibilities, and what actions to take.
Adapting & Learning
Another promising feature of AI Agents is their ability to learn and adapt. They revise plans as per new information and recognize patterns over time to make better decisions. AI Agents also learn from user interactions and improve their performance based on human feedback.
Tool Use:
AI Agents can achieve goals by using tools and APIs that connect them to external knowledge resources and real-time data.
Proactive:
An AI Agent is proactive; that is, it does not wait for commands and acts towards achieving a goal once given an initiative.
