The AI Buzzword Glossary: From Agents to Agentic AI
Every week brings a fresh wave of AI terminology. “Agentic AI,” “RAG,” “MCP,” “context engineering” — the words pile up faster than anyone can keep track of them, and half the time they’re used loosely or interchangeably.
This post is a no-jargon reference. Each term gets a plain-English definition (what it is) and a short note on what it’s for — so you can read a vendor blog or a conference talk without nodding along to words you don’t actually use yourself.
The terms are grouped by theme so related ideas sit together.
1. Core Agent Terminology
This is the cluster everyone is talking about — the shift from chatbots that answer to systems that act.
| Term | What it is | What it’s for |
|---|---|---|
| AI Agent | An LLM-powered system that can take actions — call tools, hit APIs, run code — to accomplish a goal, not just produce text | Automating multi-step tasks like data lookups, ticket resolution, or bookings |
| Agentic AI | The broader paradigm where AI plans, decides, executes, and self-corrects with some degree of autonomy | The umbrella term for moving from “assistant that talks” to “system that does” |
| Autonomous Agent | An agent that runs with minimal or no human input, looping until its goal is met | Background jobs, monitoring, “set and forget” workflows |
| Multi-Agent System (MAS) | Several specialized agents collaborating, each owning a sub-task | Complex workflows where one agent coordinates others (planner + researcher + writer) |
| Orchestration | The control layer that decides which agent, tool, or model handles each step | “Who does what next” in a multi-agent flow |
| Copilot / Assistant | A human-in-the-loop agent embedded in an app that suggests or assists rather than acting alone | Productivity inside everyday tools |
2. Architecture & Reasoning Patterns
How agents are actually wired up under the hood.
| Term | What it is | What it’s for |
|---|---|---|
| RAG (Retrieval-Augmented Generation) | Fetching relevant documents or data and feeding them to the model before it answers | Grounding answers in your own data; reducing hallucination |
| Grounding | Tying model output to authoritative source data | Trust, citations, accuracy |
| Chain-of-Thought (CoT) | Prompting the model to reason step-by-step before answering | Better results on math, logic, and multi-step problems |
| ReAct (Reason + Act) | A pattern where the model alternates between reasoning and tool-calling | The backbone of most agent loops |
| Reasoning Model | A model trained to “think” longer before responding | Hard problems that need deliberation |
| Tool Use / Function Calling | The model invoking external functions or APIs in a structured way | How agents actually do things rather than just describe them |
| Context Engineering | Deliberately designing everything that goes into the model’s context window | The evolution of “prompt engineering” for agents |
3. Models & Foundations
The engines that power everything above.
| Term | What it is | What it’s for |
|---|---|---|
| LLM (Large Language Model) | A model trained on massive text to generate language | The core engine of most GenAI |
| SLM (Small Language Model) | A compact, cheaper, faster model | On-device, low-latency, cost-sensitive tasks |
| Foundation Model | A large pre-trained model reusable across many tasks | The base you build or fine-tune on |
| Frontier Model | The newest, most capable models at the cutting edge | Benchmark-leading and demanding workloads |
| Multimodal | Handles a mix of text, images, audio, and video | Document understanding, vision tasks, voice |
| GenAI (Generative AI) | Umbrella term for AI that creates content | The whole category |
4. Data & Memory
Where agents get their knowledge — and how they remember.
| Term | What it is | What it’s for |
|---|---|---|
| Embeddings | Numeric vector representations of text or data that capture meaning | Semantic search, similarity, RAG retrieval |
| Vector Database | A store optimized for embeddings | Fast semantic lookup that powers RAG |
| Knowledge Source | The grounding data an agent draws on | Accurate, domain-specific answers |
| Memory | Persistence of context across turns or sessions | Personalization and long-running agents |
5. Protocols & Interoperability
The newest layer — standards that let agents and tools talk to each other without custom glue code.
| Term | What it is | What it’s for |
|---|---|---|
| MCP (Model Context Protocol) | An open standard introduced in late 2024 for connecting AI models to external tools and data sources | Plug-and-play connectors — often described as “USB-C for AI tools” |
| A2A (Agent2Agent) | An open protocol introduced in 2025 (now under the Linux Foundation) for agents to discover, message, and delegate to one another | Cross-vendor collaboration between agents |
| Connector / Plugin | A packaged integration giving an agent access to a specific system | Extending agents to enterprise systems |
MCP vs. A2A in one line: MCP connects an agent to its tools and data; A2A connects an agent to other agents. They’re complementary, not competing.
6. Build & Tuning
The practical side — making, shaping, and trusting models.
| Term | What it is | What it’s for |
|---|---|---|
| Fine-tuning | Further training a model on your own data | Specialized tone, domain, or behavior |
| Prompt Engineering | Crafting effective instructions | Steering model output |
| Inference | Running the model to produce output (as opposed to training it) | The “cost per use” side of the equation |
| Guardrails | Safety and policy filters around inputs and outputs | Preventing harmful or off-policy responses |
| Evals (Evaluations) | Systematic testing of model or agent quality | Measuring accuracy and catching regressions |
| Hallucination | Confident but wrong or fabricated output | The core problem that grounding and RAG try to solve |
How It All Fits Together
If you read the tables top to bottom, a rough mental model emerges:
- A foundation model (an LLM) provides the raw intelligence.
- Wrap it in a reasoning pattern like ReAct, give it tool use, and you have an agent.
- Ground it with RAG over a vector database so it answers from real data.
- Connect it to systems via MCP, and to other agents via A2A.
- Coordinate several of them through orchestration to build a multi-agent system.
- Keep it honest with guardrails and measure it with evals.
That progression — from a single model to a coordinated, tool-using, self-correcting system — is essentially what people mean when they say agentic AI.
Know a buzzword that should be on this list? The vocabulary keeps growing — this glossary will too.
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