AI agents are autonomous systems that perceive their environment, reason about goals, and take actions to achieve them. They represent the next evolution beyond chatbots — from reactive Q&A to proactive task completion.

From Chatbots to Agents

The Agent Loop

At its core, every agent follows this loop:

Observe → Think → Plan → Act → Observe result → Repeat until done

1. Observe: Receive input from the user or environment
2. Think: Analyze the situation and available information
3. Plan: Decide what action(s) to take next
4. Act: Execute the chosen action (tool call, code execution, etc.)
5. Observe result: Check the outcome of the action
6. Repeat: Continue until the task is complete or the agent needs help

Types of AI Agents

ReAct agents: Interleave reasoning and acting. The most common pattern. The LLM thinks out loud, decides on an action, executes it, then reasons about the result.

Plan-and-execute agents: Create a full plan upfront, then execute steps sequentially. Better for complex multi-step tasks where the overall strategy matters.

Reflexion agents: After completing a task, reflect on what went well and what didn't. Use insights to improve future performance.

Tool-use agents: Specialized in calling external APIs and tools based on user requests. The LLM acts as an intelligent router.

Real-World Agent Examples

• Devin (Cognition): Autonomous software engineer that plans, writes, and debugs code
• AutoGPT: General-purpose agent that breaks down goals into tasks
• Operator (OpenAI): Browser-based agent that navigates websites to complete tasks
• GitHub Copilot Workspace: Plans and implements code changes from issue descriptions
• Replit Agent: Builds applications from natural language descriptions