Minimum vocabulary
If LLM, agent, MCP, and harness all sound like the same thing, you are not alone. Before talking about vibe coding, it is worth understanding the new terms.
LLM (Large Language Model): a large language model. It predicts the next stretch of text from what it has read. Chatbots and coding agents usually run on LLMs underneath.
Inference: running the model to produce an answer. Training is a different story. In day-to-day product work, almost everything you see is inference.
Token: a chunk of text the model processes. Long words can become several tokens. Context windows and cost are usually measured in tokens.
Context: what the model “sees” in that turn: prompt, files, history, rules. More context helps; noisy context hurts.
Prompt: the request in natural language (or close to it). It can be short or a long brief.
Code context: repository slices (open files, diffs, symbols) the tool attaches so the answer fits your code.
ADE: AI development environment for the AI agent, terminal, and integrations.
CLI: command-line interface. CLI agents read files, run commands, and change the repo directly in the terminal.
Coding agent (code agent): an agent specialized in software. It reads the repo, edits, tests, and iterates until the task passes validation.
MCP (Model Context Protocol): a standard for connecting models to tools and context sources in a structured way. Useful, but a connected tool without governance also widens the risk surface.
Harness: the environment around the model (instructions, tools, limits, validation). It turns “a model that answers” into “a system that operates”.