Local AI Glossary

A program that uses a language model to reach a goal in multiple steps: it decides which tools to call (search, calculator, terminal, browser), reads the results, and decides the next step until the task is done or fails. The difference from a chat: the agent acts on the environment, and its behavior depends on the harness — the code around it — that assembles observations and actions.

Customer Relationship Management: software that manages customer relationships — contacts, quotes, negotiations, history. The quality of CRM data determines how much a business can automate sales and follow-up.

Phenomenon where an LLM generates false or invented information with high confidence. Hallucinations are a well-known issue with language models: they can invent facts, quotes, or non-existent references. RAG and fine-tuning are techniques to reduce them.

Key-Value cache. The memory a transformer model uses to remember the conversation: every new token adds a small entry that grows with context length and the number of users served in parallel. It is the VRAM component that varies with your settings and must be accounted for per user.

Computational system inspired by the human brain, composed of layers of artificial neurons. LLMs are based on the Transformer architecture, a specific family of neural networks optimized for language. Each model is defined by its architecture, weights, and the dataset it was trained on.

Software whose source code is public and can be studied, modified, and redistributed. In AI, open-source models like Llama, Mistral, and Gemma can be downloaded and run locally, enabling full control, privacy, and customization.

A model is open-weight when its weights are downloadable and its use and redistribution are governed by a license. Unlike open source, the code, data, and training method are not necessarily public. Most modern models are open-weight.

Numerical value inside an AI model that is learned during training. A 7B (billion) parameter model has 7 billion values that determine its behavior. More parameters generally mean greater capabilities, but require more computational resources.

A language model's ability to call external functions (APIs, calculator, database, browser) while answering: the model produces a structured call with the right parameters, the software runs the function and returns the result to the model, which continues its reasoning. It is the mechanism that turns a chat into an agent: gpt-oss, Qwen and Gemma support it, and engines like vLLM and Ollama handle it natively.

A sequence of repeatable steps that connects systems together: an event arrives, operations run in order, a result is produced. In automation platforms, the workflow is built visually by connecting nodes (email → AI model → CRM → notification).