AI Reference 059 Large Language Models Wikipedia guided Primary sources linked

AI Agents

Definition

An AI agent is a system in which a model can select or plan actions over multiple steps, usually with tools, memory and feedback from an environment. The language model is only one part of the agent; orchestration, permissions, tools and stopping conditions are equally important.

What is an AI agent?

An AI agent is a software system that uses an AI model to choose actions over multiple steps rather than producing only one response. An agent may plan, call tools, inspect results, revise its plan and stop when a goal or stopping rule is reached.

What components an agent needs

Practical agents include a model, instructions, tools, state or memory, an execution loop and permission boundaries. Some also use separate planners, evaluators or smaller specialist models.

Why agents are different from chatbots

A chatbot may simply generate text. An agent can cause external changes through tools—for example updating a ticket or editing code. That makes reliability and permissions much more important because a poor answer can become a poor action.

Related terms, defined

Large Language Models Mixture-of-Experts Models A mixture-of-experts model contains multiple expert subnetworks and routes each token or input through only a subset of them. This can increase total parameter capacity without activating every parameter on every forward pass. Large Language Models What Is a Large Language Model? A large language model is a statistical model trained on large collections of text or multimodal data to predict and generate token sequences. Modern LLMs are usually transformer-based and are adapted to conversation, coding, tool use and other tasks through post-training and application-level systems. Large Language Models LLM Pretraining LLM pretraining exposes a model to large datasets and optimizes a predictive objective, commonly next-token prediction, before later post-training or adaptation. Large Language Models Fine-Tuning Language Models Fine-tuning continues training a pretrained model on a narrower dataset or objective so it performs better for a particular domain, task or behavior. Large Language Models RLHF and Preference Training Reinforcement learning from human feedback uses preference information from people to train or guide models toward preferred behavior; modern post-training also uses related preference-optimization techniques.

Reference guide and primary sources

Wikipedia is used here as a terminology and history reference guide. Current model versions, institutional statistics and product-specific claims are also linked to first-party or institutional sources because those details can change faster than encyclopedia articles.

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