What is an AI Agent?

An artificial intelligence (AI) agent is like a computer program that can do tasks on its own without needing constant supervision. It figures out how to do these tasks by organizing its work and using available tools.

These AI agents help solve difficult problems in many areas, such as designing software, automating IT tasks, generating code, and even having conversations with people. They use advanced language understanding to comprehend what users say and respond accordingly. They can also decide when to use other tools to help them complete their tasks.

AI Agent Operational Cycle

How AI agents work ?

AI agents, often called LLM agents, are systems powered by large language models (LLMs) that go beyond traditional LLMs by integrating tool-calling capabilities. This allows them to access real-time information, optimize workflows, and autonomously create subtasks to achieve complex goals. Unlike traditional LLMs, which are limited by the data they were trained on, agentic technology enables dynamic adaptability and decision-making by interacting with external tools and environments.

These agents also learn and improve over time by storing past interactions in memory and planning future actions. This ability fosters personalized experiences, as the agent adapts to user preferences and provides more tailored responses. By leveraging feedback mechanisms from users or other AI agents, they refine their performance and align more closely with user expectations.

How are AI Agents made?

Creating an AI agent is a bit like building a super-smart robot that can help with specific tasks. First, developers decide what they want the agent to do, like answering questions or helping with customer service. Then, they choose the right tools and technologies, often using programming languages like Python. Next, they design the agent's brain using something called machine learning models, which are like recipes that teach the agent how to think and act. These models are trained on lots of data, like text or images, so the agent can learn from examples. Once the agent is built, it's tested to make sure it works correctly and then deployed where people can use it. Over time, the agent might get updates to make it even smarter and more helpful.