AI Agents Introduction

Introduction

An AI agent is an LLM that can use tools and take actions in a loop until a goal is achieved. Instead of a single response, the agent thinks, acts, observes, and repeats.

Beginner analogy: Like an intern who can use Slack, Gmail, your CRM and a browser to complete a task end-to-end without you watching every step.

Understanding the topic

Core concepts to understand:

• Loop: Think → Act (tool) → Observe → repeat.
• Tools = functions the LLM can call (search, DB, API, code exec).
• Stops when goal achieved or max-steps reached.
• Frameworks: LangGraph, OpenAI Assistants, Vercel AI SDK, CrewAI.

Syntax reference

Visual workflow / architecture:

      ┌──────── Agent Loop ────────┐
      │                            │
Goal ─►  THINK (LLM reasoning)     │
      │       │                    │
      │       ▼                    │
      │   ACT (call tool)          │
      │       │                    │
      │       ▼                    │
      │  OBSERVE (tool result)     │
      │       │                    │
      │       └────► back to THINK │
      │                            │
      │   ── done? exit ──         │
      └────────────────────────────┘

Real-world use

Devin (Cognition AI), OpenAI Operator, Claude Computer Use, Replit Agent, Lovable's own AI agent are agents in production.

Best practices

• Start with strict step limits — runaway loops are expensive.
• Always log every tool call for debugging.
• Add a 'human approval' gate before destructive actions.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. What is an AI agent?
• Q2. Walk through the think-act-observe loop.
• Q3. Why do agents need step limits?