AI Agents Explained
What 'agents' actually are, what they can do today, and how to start using them.
Overview
An AI agent is just an AI that takes actions in a loop — read, decide, act, observe, repeat — instead of giving you a single answer. This explainer cuts the hype and shows what's realistic today.
Step by step
- 1
Understand the loop
Agent receives a goal → uses tools (search, browser, code, API calls) → observes results → decides next step → repeats until the goal is done or it gives up.
- 2
Start with ChatGPT or Claude's built-in agent modes
Both can browse the web, run code, and use computer-use tools. This is the easiest on-ramp.
- 3
Add a no-code workflow tool
Zapier Agents, Make scenarios with looping, or n8n agent nodes. You define the tools, the agent decides when to use them.
- 4
Give it ONE goal at a time
'Research the top 10 competitors in X market and put them in this Google Sheet with revenue, founding year, and ICP.'
- 5
Set a budget and a stop condition
Agents can loop forever. Always cap the steps, tool calls, and dollars.
Tips
- Best first agent: research + spreadsheet population. Low risk, huge time save.
- Watch the trace logs — you'll learn how the model 'thinks' and where to tighten your prompt.
- Pair agents with a human approval step for anything irreversible.
Common mistakes
- • Treating agents like magic. They're still just LLMs in a loop and they still make mistakes.
- • Giving an agent write access to production systems on day one.
- • Not logging tool calls — when something goes wrong you have no audit trail.