What are AI Agents?

Introduction

An AI agent is a software entity that perceives its environment, decides on an action and executes it — repeatedly — to achieve a goal. With LLMs, the 'decision' is a model call and the 'environment' is your tools, code and APIs.

Beginner analogy: Roomba is a physical agent (sense → decide → move). LLM agents are digital Roombas (sense logs → decide tool → call API → observe).

Understanding the topic

Core concepts:

• Perception: prompt, tool result, memory recall.
• Decision: LLM picks next action (often a tool).
• Action: tool call, code execution, message.
• Loop: until goal achieved or step limit hit.
• Differs from a chatbot: agents act, chatbots reply.

Syntax reference

Visual workflow / architecture:

User Request
      │
      ▼
┌──────────────┐
│   Planner    │  decomposes the task
└──────┬───────┘
       │ subtasks
       ▼
┌──────────────┐
│  Tool Caller │──► External APIs / DBs / Search
└──────┬───────┘
       │ observations
       ▼
┌──────────────┐
│  Reasoner    │  reflects · evaluates
└──────┬───────┘
       │ next step or "done"
       ▼
┌──────────────┐
│   Memory     │  short-term + long-term
└──────┬───────┘
       ▼
   Final Response

Real-world use

Devin, Operator, Copilot Workspace, AutoGPT, BabyAGI, CrewAI bots, LangGraph apps — all follow this pattern.

Best practices

• Start with one tool, one goal, max 10 steps.
• Log every decision; you'll need traces to debug.

Common mistakes

• Coupling perception and action — split them for testability.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Define an AI agent in one sentence.
• Q2. Three components every agent has.
• Q3. Difference between an agent and a chatbot.
• Q4. Scenario: design an agent to triage incoming emails.