35 terms · Plain English
The agentic AI glossary.
Every term you’ll meet in the wild — defined in one breath each, no jargon defining jargon. Updated June 11, 2026.
- Agent
- A language model wrapped in a role, tools, and memory, running in a loop that plans, acts, and checks its work until a goal is met. The unit of delegation in agentic AI. See how agents work.
- Agentic AI
- AI that pursues goals with minimal supervision rather than answering single prompts — it plans steps, executes them, and verifies results. The full story: What is agentic AI?
- Agent loop
- The core cycle behind every agent: plan → act → observe → repeat. A chatbot makes one pass; an agent may circle dozens of times before delivering.
- AGI (Artificial General Intelligence)
- Hypothetical AI matching human capability across virtually all cognitive work. Not the same as agentic AI, which exists today and handles specific, bounded tasks.
- Autonomy levels
- The dial between “drafts and waits for approval” and “executes the whole plan unsupervised.” Consumer agents deliberately sit toward the supervised end: the agent does legwork, you keep judgment calls.
- Chain of thought
- A model reasoning step by step — often visibly — before answering. Improves accuracy on multi-step problems and makes the reasoning auditable.
- Claude
- Anthropic’s family of frontier AI models. The June 2026 lineup spans Haiku 4.5, Sonnet 4.6, Opus 4.8, and the flagship Fable 5.
- Claude Fable 5
- Anthropic’s Mythos-class frontier model, publicly released June 9, 2026 — its most capable generally available model, with safeguards that reroute a small set of high-risk topics. Explainer: What is Claude Fable 5?
- Context window
- The working memory of a model — everything it can “see” at once: your brief, the conversation, tool results. Agents manage this budget carefully on long tasks.
- Custom agent
- An agent you define yourself — persona, tone, expertise, standards — rather than using a built-in specialist. In Agentic AI, custom agents are unlimited with PRO.
- Fine-tuning
- Further training a model on specific data to specialize it. Distinct from role prompting, which specializes behavior without changing the model.
- Foundation model
- A large model trained on broad data that many different products build upon. The “engine” layer beneath agents and apps.
- Frontier model
- A model at the current capability ceiling — in mid-2026, the class that includes Claude Fable 5. Agents improve disproportionately with frontier upgrades because errors compound across steps.
- Generative AI
- AI that produces content — text, images, code, audio — in response to prompts. The engine inside agentic systems, but without the goal-pursuit machinery. Comparison: agentic vs. generative.
- Guardrails
- Constraints that keep an agent inside intended behavior: forbidden actions, output standards, step budgets, content policies. The reason “autonomous” doesn’t mean “unsupervised.”
- Hallucination
- A model stating something false with confidence. Agents reduce it via self-checking and citations, but don’t eliminate it — verify before acting on important claims.
- Inference
- Running a trained model to produce output (as opposed to training it). What you’re paying for, compute-wise, every time an agent thinks.
- LLM (Large Language Model)
- A neural network trained on vast text corpora to understand and generate language. The technology underneath every modern agent.
- Maker–checker
- A two-agent pattern: one produces, the other critiques, the first revises. Cheap insurance for code, contracts, and anything where errors are expensive.
- MCP (Model Context Protocol)
- An open standard for connecting AI models to tools and data sources — a universal adapter so any agent can plug into any system without custom wiring.
- Memory
- What an agent retains: short-term (the running task context) and sometimes long-term (durable notes across sessions). Keeps step fourteen consistent with step two.
- Multi-agent system
- Multiple specialized agents sharing one session, reading each other’s output, and dividing labor — research, strategy, writing, planning. Why it wins: two agents beat one.
- Multimodal
- Able to work across media types — text, images, documents, audio — not just words. Lets agents read screenshots, charts, and PDFs.
- Orchestration
- The coordination layer of a multi-agent system: who works when, what each agent sees, how outputs combine. In consumer apps, orchestration ships pre-built.
- Panel
- A multi-agent pattern where specialists answer the same question from different angles, then reconcile. Suits decisions and evaluations.
- Pipeline
- A multi-agent pattern where each agent builds on the previous one’s output — researcher → strategist → writer → planner. Suits staged work like launches.
- Prompt
- The instruction you give a model. For agents, the better word is brief: goal, constraints, definition of done. Templates: the prompts playbook.
- Prompt engineering
- The craft of writing instructions that get reliably good output. With agents, it converges on writing good briefs — the same skill as delegating to people.
- RAG (Retrieval-Augmented Generation)
- Fetching relevant documents and giving them to a model before it answers, so responses are grounded in real sources instead of memory alone.
- Reasoning model
- A model variant that spends extra computation “thinking” before answering — stronger on math, code, and planning. Most frontier models now blend this in.
- Role (system prompt)
- The standing instructions that define who an agent is and what good output looks like. Roles are behavioral constraints, not costumes — a defined specialist outperforms a vague generalist.
- Team session
- Agentic AI’s multi-agent mode: up to four experts on one brief, each seeing the running conversation and adding only what’s missing. Like a smart meeting, in your pocket.
- Token
- The unit models read and write — roughly ¾ of an English word. Context windows and API prices are measured in tokens.
- Tool use (function calling)
- A model invoking external capabilities — search, code execution, file reading — instead of just describing them. How agents touch reality; results feed the agent loop’s “observe” step.
- Workflow vs. agent
- A workflow follows a fixed, predefined sequence; an agent chooses its own steps based on what it observes. Workflows are predictable; agents are adaptable. Real products mix both.
Agentic AI — Vocabulary is nice; delegation is nicer. Meet your agent team. Get the app free
