AI agent terminology in 2026: 25 terms every buyer should know

The 25 AI agent terms every buyer should know in 2026 — what they mean in plain English and why they matter.

For the full glossary with all 80+ terms see /glossary.

Foundational (5 terms)

Agent — Software powered by an LLM that perceives, plans, and acts across multiple steps. Different from a chatbot because it takes action, not just generates text.

Agentic AI — AI systems that act autonomously rather than just respond to prompts. The umbrella term for the 2026 wave.

LLM (Large Language Model) — The neural network at the heart of every agent. GPT-5, Claude, Gemini are LLMs.

Agentic loop — The core pattern: observe → reason → act → observe, repeated until done.

Agentic workflow — A workflow where AI decides next steps dynamically, not a fixed if-this-then-that automation.

Autonomy (4 terms)

Autonomous agent — Plans and executes without asking for approval between steps. Examples: Devin, Manus.

Semi-autonomous agent — Plans and acts unsupervised but pauses at irreversible actions. Examples: Cursor, Cline.

Copilot — Suggests; waits for human to accept. Examples: GitHub Copilot inline, ChatGPT chat.

Human-in-the-loop — Pattern where agent pauses for human approval at key checkpoints.

Capabilities (6 terms)

Tool use — Agent invokes external functions (APIs, browsers, code). The capability that turns LLMs into agents.

Function calling — The API mechanic for tool use. Model emits structured JSON; runtime executes.

Browser use — Agent drives a web browser like a human (clicks, types, navigates).

Code execution — Agent writes and runs code in a sandbox.

RAG (Retrieval-augmented generation) — Agent retrieves relevant docs before answering. Grounds outputs in your data.

Memory — Agent remembers across sessions. Critical for personal assistants and long-running agents.

Infrastructure (5 terms)

MCP (Model Context Protocol) — Open standard for connecting agents to tools. Anthropic-published, now industry standard.

Vector database — Storage for embeddings used in RAG and semantic search.

Vector embedding — Dense numerical representation of text/image/audio used for semantic similarity.

Context window — Maximum tokens the model considers at once. 200K-1M typical in 2026.

Prompt caching — Cost optimization that reuses computation for stable prompt prefixes. 50-90% discount.

Reasoning & architecture (3 terms)

Chain of thought — Model reasons step-by-step before answering. Improves accuracy.

Reasoning model — Models that produce internal thinking before responding. Examples: o3, Claude with extended thinking.

Test-time compute — Spending more compute at inference time for better answers without retraining.

Evaluation (2 terms)

AI evals — Systematic test suites for AI systems. Unit tests for prompts and agents.

LLM observability — Tracing every LLM call (prompt, response, cost, latency). Mandatory for production.

What you actually need to know as a buyer

For purchasing AI agents in 2026, focus on these:

1. Autonomy tier (copilot / semi-auto / autonomous)
2. Capabilities (tool use, RAG, voice, browser use)
3. Pricing model (subscription, per-task, BYO-key)
4. Key metrics (deflection rate, PR merge rate, etc.)
5. MCP support (for coding agents specifically)

The rest is for builders, not buyers.

The verdict

25 terms cover 95% of AI agent buyer conversations in 2026. The other 5%? Read our full glossary which has 80+ entries.

For deeper guides see The 15 best AI agents in 2026, AI agent vs LLM, and AI agent vs chatbot.