AI agents are systems where a language model can autonomously decide what actions to take, use tools, and work through multi-step tasks — rather than simply responding to a single prompt.

Chatbots vs Agents

A chatbot responds to your messages. An agent takes your goal and works toward it independently, deciding which steps to take, which tools to use, and when to ask for clarification.

• Chatbot: "Summarize this email" → gets a summary
• Agent: "Clear my inbox" → reads emails, drafts replies, archives newsletters, flags urgent items, asks you to approve before sending

Levels of AI Agency

Think of it as a spectrum:

1. Assisted — AI suggests, human decides (autocomplete, Copilot suggestions)
2. Semi-autonomous — AI executes a plan, human approves key steps (Cursor Composer, ChatGPT with tools)
3. Autonomous — AI plans and executes independently, reporting results (Devin, AutoGPT)
4. Fully autonomous — AI operates continuously without human oversight (theoretical, not yet reliable)

Most practical AI agents today operate at levels 2-3: they can plan and execute multi-step tasks but benefit from human oversight at critical decision points.

How Agents Work: The ReAct Loop

Most AI agents follow a loop called ReAct (Reasoning + Acting):

1. Observe — Receive a task or new information
2. Think — Reason about what to do next
3. Act — Execute a tool or take an action
4. Observe — See the result
5. Repeat until the task is complete

Real-World Agent Examples

• Coding agents (Cursor Agent, Claude Code, Devin) — Read your codebase, write code, run tests, fix errors, submit PRs
• Research agents (Gemini Deep Research, Perplexity) — Search multiple sources, synthesize findings, create reports
• Customer support agents — Read customer history, look up orders, draft responses, escalate when needed
• Data analysis agents — Connect to databases, run queries, create visualizations, explain findings
• Personal assistants — Manage calendar, draft emails, book travel, set reminders

Why Agents Matter

Agents represent the next evolution of AI productivity. Instead of crafting perfect prompts for individual tasks, you describe a goal and the agent figures out the steps. This fundamentally changes how we interact with AI — from tool to collaborator.