TLDR:
An AI agent is an LLM-powered system that pursues a goal by autonomously planning, calling tools, observing results, and iterating—rather than producing a single response to a single prompt. AI agents are the dominant AI architecture pattern driving enterprise adoption in 2025-2026.
What Distinguishes an Agent from a Chatbot
A chatbot responds to a prompt with a single output. An agent has agency over a multi-step task: it can decide what to do next, call external tools (web search, code execution, API requests, database queries), observe the results, update its plan, and continue until the goal is achieved or fails. Modern agents combine LLM reasoning with tools like file systems, browsers, and code execution to perform substantial knowledge work autonomously.
Core Architecture Components
An agent system typically includes: the LLM (the reasoning engine), tools (external capabilities the agent can invoke), memory (short-term context and long-term knowledge), planner (decomposes goals into steps), executor (runs the planned actions), and observer (interprets results). Standards like the Model Context Protocol (MCP) have emerged as the dominant interface for connecting agents to tools and data sources.
Use Cases and Risks
Enterprise agent use cases include: coding agents (Claude Code, Cursor, Devin), customer support automation, research and synthesis (deep research products), business process automation, and increasingly agentic workflows for sales, marketing, and finance teams. Risks include unbounded action loops, tool misuse, prompt injection through external content, hallucination amplification across multi-step tasks, and difficulty auditing autonomous decisions. Production agents require careful permission scoping, observability, and human-in-the-loop checkpoints for high-stakes actions.