An AI system that goes beyond simple question-answering by autonomously planning, using tools, executing multi-step tasks, and making decisions to achieve goals — often interacting with external systems and APIs on behalf of the user.
In Depth
An AI Agent is a system where a language model acts as the 'brain' that orchestrates a loop of reasoning, planning, and action. Unlike a standard chatbot that produces a single response to each message, an agent can break down a complex goal into subtasks, decide which tools to use (web search, code execution, database queries, API calls), execute those actions, observe the results, and iterate until the goal is achieved. This plan-act-observe cycle allows agents to accomplish tasks that no single model call could handle.
The agent paradigm emerged from research showing that LLMs, when given access to tools and prompted to reason step-by-step, could solve complex, multi-step problems. Frameworks like LangChain, AutoGPT, CrewAI, and OpenAI's Assistants API provide infrastructure for building agents. A typical agent has: a language model for reasoning, a set of tools it can call (search engines, calculators, code interpreters, APIs), a memory system for tracking progress, and a planning mechanism that decides what to do next based on results so far.
AI Agents represent the frontier of practical AI deployment. While chatbots handle conversation, agents handle workflows. A coding agent can write, test, debug, and deploy code autonomously. A research agent can search, read, synthesize, and cite sources. A customer service agent can look up orders, process refunds, and escalate issues. However, agents also introduce new risks: autonomous execution can propagate errors, exceed intended scope, or take costly irreversible actions. Designing reliable, safe, and controllable agents — with appropriate human oversight — is one of the most important challenges in applied AI.
AI Agents use language models as reasoning engines to autonomously plan, use tools, and execute multi-step tasks — moving AI from passive answering to active problem-solving.