An AI agent is a software system powered by a large language model that can autonomously plan actions, use external tools (APIs, databases, browsers, code interpreters), and complete multi-step tasks with minimal human intervention.
A basic chatbot responds to one message at a time. An AI agent goes further: it breaks a goal into subtasks, decides which tools to call, interprets the results, and adjusts its plan based on what it learns along the way. For example, a research agent might search the web, read several pages, cross-reference data, and compile a summary, all from a single user prompt.
The agent loop typically follows a pattern: observe (read the current state), think (decide the next action), act (call a tool or generate output), and repeat until the task is complete. Frameworks like LangChain, AutoGen, and CrewAI provide scaffolding for building agents. The LLM serves as the "brain" that drives reasoning, while the tools give it capabilities beyond text generation.
Agents vary widely in autonomy. Some require human approval before each action. Others run fully autonomously, making decisions and executing tasks without checkpoints. The right level of autonomy depends on the stakes involved and the reliability of the underlying model.
AI agents represent the next evolution beyond simple chatbots. They can automate complex workflows, from code review to data analysis to customer support. As agents become more capable and reliable, they are changing how developers build software and how businesses operate. Tracking which agents exist, what they can do, and how they are evolving is increasingly important.
The AI Agent Tracker monitors 34 AI agents across 7 categories with live status updates. The AI Hub panel on the dashboard tracks API calls from AI agents accessing TerminalFeed data. Read our articles on how AI agents browse the web and building websites for humans and AI agents.