Agents are LLMs given tools. A chatbot answers your question; an agent receives a goal, breaks it into steps, and executes each step using tools (web search, code execution, API calls, file I/O) until the goal is reached. Examples: a research agent that searches the web, reads sources, and writes a synthesis; a coding agent that reads your codebase, plans a refactor, and submits a PR; a sales agent that drafts outreach, sends it via Gmail, and logs to CRM. The big challenge: error compounding. A 10-step agent with 90% reliability per step is only 35% reliable end-to-end. By 2026, agents are useful for narrow domains (coding, research, customer support automation) but still unreliable for broad open-ended tasks. The MCP (Model Context Protocol) standard is making agent-tool integration much cleaner.
GLOSSARY
What is AI Agent?
An AI system that uses LLMs not just to chat, but to plan, take actions (call APIs, run code, browse the web), and pursue multi-step goals autonomously.
RELATED TERMS
LLM (Large Language Model)
An AI system trained on massive text datasets to predict and generate human-like text — the technology behind ChatGPT, Claude, Gemini, and most modern AI chatbots.
MCP (Model Context Protocol)
An open standard launched by Anthropic in 2024 that defines how AI models communicate with external tools and data sources.
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