Agents 101: Reasoning, Actions & Autonomy

Short summary

An AI agent uses an LLM to reason about goals and autonomously execute action sequences by calling tools, observing results, and iterating until the goal is reached. Unlike chatbots (which don't act) and workflows (which don't decide), agents adapt their path based on reasoning about current state. The ReAct pattern dominates modern agent architecture: the LLM alternates between reasoning steps and action calls, enabling self-correction and interpretable decision-making.

• •Agents combine LLM reasoning with tool execution to autonomously pursue multi-step goals
• •ReAct pattern: LLM alternates between reasoning steps and tool calls, enabling self-correction
• •Core components: LLM (decision-maker), tools (action layer), memory (state), control loop (orchestration)

Generated with AI, which can make mistakes.