Term
AI Agent
An AI agent combines a language model with tools and works toward a goal across multiple steps — typically in a loop of observing, planning and acting.
AI Agent — explained in more detail
An AI agent combines a language model with tools and works toward a goal across multiple steps. At its core runs a loop of observing, planning and acting: the model receives a task, calls tools (file operations, search, shell commands), evaluates the results and plans the next step — until a stop condition is met. The building blocks are therefore an LLM, tool calls, context management and a clear termination criterion.
Example / Practical context
Coding agents like Claude Code, Cursor Composer or Aider follow exactly this pattern: read a file, plan a change, execute the edit, run tests, read the result, fix if necessary. Autonomous browser agents fit the same mold — they navigate pages, click elements, extract data and decide for themselves when the goal has been reached.
Distinction from related concepts
A single tool call from an LLM does not make an agent — what matters is the multi-step loop and the model’s own control over the stop condition. Classic workflows with hard-wired steps (such as a “RAG pipeline”) are not agents either, since the model does not determine the order itself.
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GlossarEnsemble / Multi-Model Orchestration
Ensemble means combining several deliberately varied LLM runs or models whose findings complement each other. Multi-model orchestration drives these runs via orchestrators with sub-agents, so the union of results is larger than any single run.
LexikonFunction Calling / Tool Use
How an LLM uses tools: define a tool as a schema, the model picks the function and arguments, the result returns to the chat — the basis of every agent.