AI Agent
Definition
An AI agent is an LLM-powered system that autonomously plans, selects tools, executes multi-step tasks, and loops until a goal is achieved -- without requiring step-by-step human instruction. AI agents extend a language model''s capability from answering questions to taking actions: writing code, querying APIs, browsing the web, and updating databases.
The core difference between a chatbot and an AI agent is autonomy over tool use. A chatbot responds. An agent acts: it decides which tools to call, inspects results, and continues until the goal is met or it determines it cannot proceed.
Agent components
- LLM core -- the reasoning engine that plans and decides next steps
- Tool registry -- functions the agent can call (search, database query, API calls, code execution)
- Memory -- short-term context window + optional long-term vector memory
- Loop controller -- runs the plan-act-observe cycle until task completion
Practical agent examples
Invoice processing agent that reads email attachments, extracts line items, validates against PO, and routes for approval. Or a research agent that searches internal documents, the web, and a CRM to draft a client briefing.
Related terms
RAG (Retrieval-Augmented Generation)
Retrieval-augmented generation (RAG) is an AI architecture that supplements a large language model's static training knowledge with real-time retrieval from a private or external knowledge base. RAG reduces hallucinations by grounding LLM responses in verified source documents, making it the standard pattern for enterprise AI assistants built on proprietary data.
LLM (Large Language Model)
A large language model (LLM) is a deep-learning model trained on billions of text tokens to predict and generate human-readable language. LLMs such as GPT-4, Claude, and Gemini power chatbots, document summarization, code generation, and AI workflow automation -- and serve as the reasoning engine inside RAG systems and AI agents.
MCP (Model Context Protocol)
Model Context Protocol (MCP) is an open standard introduced by Anthropic in 2024 that defines how AI models connect to external tools, data sources, and services through a unified interface. MCP lets an AI agent call database queries, web searches, file systems, and custom APIs using a single protocol instead of bespoke tool integrations for every data source.
Agentic AI
Agentic AI refers to AI systems that operate autonomously over extended task sequences -- planning actions, invoking tools, observing results, and re-planning until a goal is complete without step-by-step human guidance. Unlike single-turn chatbots, agentic systems can execute workflows that span minutes or hours, touching multiple APIs, databases, and services.
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