The AI agents glossary
Every core term in agentic AI, defined in plain English — with how it works, a concrete example, and a link to the deep dive. Bookmark it as your reference for building and writing about AI agents.
- 20+ terms defined
- Plain English
- Updated 2026
New fields invent new vocabulary, and agentic AI has invented a lot of it fast. This glossary cuts through the jargon: each entry gives a precise, jargon-light definition of a term you'll meet while building or evaluating AI agents, then links to a full guide when you want depth. Start anywhere — the terms below are grouped so related concepts sit together.
Core concepts
The building blocks — what an agent is, the model that powers it, and the limits it works within.
- AI agent
- Software that perceives, reasons, plans, calls tools, and acts autonomously toward a goal in a loop.
- Agentic AI
- The paradigm of AI systems that act with agency — pursuing goals via reasoning and tools, not just generating output.
- Large language model (LLM)
- A neural network trained on vast text that predicts tokens — the reasoning engine inside most agents.
- Inference
- Running a trained model to generate outputs from inputs; the cost and latency of every model call.
- Context window
- The maximum number of tokens a model can consider at once — a key constraint and cost driver for agents.
- Autonomous agent
- An agent that pursues goals with minimal human steering — see the levels-of-autonomy guide.
How agents think and act
The patterns and mechanisms that turn a language model into something that reasons and takes action.
- ReAct (Reasoning + Acting)
- A pattern that interleaves Thought → Action → Observation so an agent reasons, acts, and re-reasons.
- Chain-of-thought
- Prompting a model to reason step by step before answering, improving multi-step accuracy.
- Prompt engineering
- Designing prompts and system instructions to steer model behavior reliably.
- Hallucination
- When a model produces plausible but false or unsupported information; reduced with grounding and verification.
- Tool calling
- An agent's ability to invoke external tools and APIs to take actions and fetch data.
- Function calling
- The mechanism where an LLM emits a structured call (name + JSON args) the runtime executes.
How agents know things
Grounding answers in real data and remembering across steps and sessions.
- Retrieval-augmented generation (RAG)
- Retrieving relevant documents and adding them to the prompt so the model answers from real, current data.
- Embeddings
- Numeric vector representations of text that place similar meaning close together, enabling semantic search.
- Vector database
- A store of embeddings that retrieves the most similar ones via nearest-neighbor search.
- Agent memory
- How an agent retains information — short-term context and long-term stores — to stay coherent over time.
- Fine-tuning
- Further training a pretrained model on task or domain data to adjust its behavior, style, or skills.
Coordinating and controlling agents
Putting agents together into systems — and keeping them safe and on-policy.
- Multi-agent system
- Multiple specialized agents that coordinate — delegating, reviewing, and handing off — to solve a goal.
- Orchestration
- Coordinating control flow between an agent's steps or between agents — routing, sequencing, retries.
- Guardrails
- Safety controls that constrain agent inputs, outputs, and actions to keep behavior safe and on-policy.
- Model Context Protocol (MCP)
- An open standard for connecting models and agents to external tools and data through a uniform interface.
Want the full picture?
Each definition links to a deep-dive guide. If you're starting from scratch, read what is agentic AI first, then follow the links from there. Comparing tools or approaches? The comparisons hub puts the trade-offs side by side.
About this glossary
It's a free, plain-English reference that defines the core terms behind agentic AI — from 'AI agent' and 'agentic AI' to RAG, embeddings, tool calling, orchestration, and guardrails. Each entry gives a precise definition, explains how the concept works, shows a concrete example, and links to the deep-dive guide so you can go from a quick definition to a full understanding.
From terms to a working agent
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