What Is an AI Agent

An AI Agent is an AI system that autonomously perceives its environment, makes plans, invokes tools, and executes actions. Unlike traditional input-output LLMs, Agents are multi-turn — they loop between thinking and acting until the task is complete.

Pattern 1: ReAct

ReAct (Reasoning + Acting) is the classic Agent pattern. Each round: think about what to do next, execute a tool call, observe the result, and enter the next round.

def react_agent(query, tools, max_steps=5):
    history = [{"role": "user", "content": query}]
    for step in range(max_steps):
        response = llm.chat(history, tools=tools)
        if response.tool_call:
            result = execute(response.tool_call)
            history.append({"role": "tool", "content": result})
        else:
            return response.content
    return "Max steps reached"

Pattern 2: Plan-and-Execute

Have the LLM generate a complete plan first, then execute step by step. Best for complex multi-step tasks.

Pattern 3: Multi-Agent

Multiple Agents playing different roles collaboratively — AutoGen (Microsoft), CrewAI (role-playing teams), and MetaGPT (simulating software company SOPs).

ScenarioRecommended Pattern
Simple multi-stepReAct
Complex analysisPlan-and-Execute
Team collaborationMulti-Agent

Agent capability grows with its tool set. Connecting to RAG Retrieval-Augmented Generation in Practice knowledge bases is a common enhancement. See Building a Personal AI Second Brain for augmented knowledge management.