lagent

Lagent is a term used to describe autonomous software agents that rely on a large language model (LLM) as their core reasoning component. In this usage, a lagent interprets user goals in natural language, plans a sequence of actions, executes those actions through tools or APIs, and evaluates the results to decide whether further steps are needed. The term is not universally standardized and is sometimes used interchangeably with “language agent” or “LLM-based agent.”

Core concepts

A lagent typically operates in a loop: understand the objective, propose a plan, carry out actions via tools (such as web searches, data analysis, or API calls), receive feedback, and adjust the plan as necessary. It may maintain a short-term memory or scratchpad for intermediate reasoning and, in more advanced setups, long-term memory to preserve context across sessions. Tool integration is a key feature, enabling tasks from information retrieval to automation and orchestration of other software services.

Architecture and safety

Common components include an input interface, a planning or reasoning module, a tool manager, a memory layer, and safety or guardrail mechanisms. Effective lagents balance autonomy with oversight, providing logs, explainable reasoning, and the ability for human supervision when needed. Limitations include potential errors from the language model, tool misbehavior, data privacy concerns, and the need for robust monitoring.

Applications and relation to other concepts

Lagents are used for automation, data gathering, decision support, workflow orchestration, and customer-support automation. They are related to broader AI agent research and to frameworks such as Auto-GPT and similar approaches that explore autonomous task execution with language models. See also AI agent, autonomous agent, and tool-using agents.