AI Agent

An AI agent is a software system that uses a Large Language Model (LLM) to pursue a goal through multiple decisions and actions. The model can interpret the current state, plan or select a next step, use external capabilities, evaluate the result, and continue until it reaches a completion condition.

Unlike a chatbot that primarily generates conversational responses, an agent can change external state. It may search a knowledge base, call an API, edit a file, operate a user interface, or delegate work to another agent.

Core components commonly include:

• a goal and instructions supplied through the model's context;
• tool calling for accessing data and taking actions;
• an execution loop that observes, decides, acts, and incorporates feedback;
• agent memory or external state for continuity;
• agent orchestration for routing, delegation, and control flow; and
• agent evaluation for measuring task success, cost, robustness, and safety.

Not every multi-step LLM application is fully autonomous. In an agentic workflow, application code may define a fixed sequence of steps. In a more autonomous agent, the model dynamically decides which steps and tools to use.

Reusable agent skills can provide specialized procedures and resources, while dynamic tool discovery keeps large tool catalogs out of the model's context until needed.

Greater autonomy increases flexibility but also expands the failure surface. Production agents require authorization boundaries, step and cost limits, agent guardrails, human approval for consequential actions, protection against prompt injection attacks, and traces that record actions taken.