AI Agents 101

An AI agent is described as an LLM-based system that repeatedly works toward a user-defined goal through four core components: a language model that makes decisions, tools that access information or perform actions, a loop that enables multi-step reasoning and adjustment, and memory that retains the goal and prior results. Loops allow agents to respond to tool outputs, break complex tasks into sequential actions, and determine when a goal is complete or impossible, while memory—often simply conversation history—prevents repeated work and supports continuity. Tools provide access to live data and real-world actions that an LLM alone cannot perform, such as checking weather, calendars, prices, or sending messages. Simple examples include recommending clothing based on current weather, checking store hours, and scheduling meetings, while more complex agents can book travel, handle customer-support cases, or research competitors by iteratively gathering and evaluating information. Systems such as Claude Code may add guardrails, orchestration, many tools, or communication among multiple agents, but the underlying concept remains a goal-oriented LLM using tools, memory, and a loop.

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