AI Agents
AI that takes actions, not just generates text. The difference between AI that talks and AI that does.
What makes something an agent
A standard LLM can only output text. An AI agent can take actions.
Agents are language models given tools — the ability to browse the web, run code, read files, call APIs, send emails, fill forms. They can perceive their environment, make decisions, take actions, and observe the results of those actions.
The difference: a base LLM can write an email. An agent can write the email, look up the recipient's address, and send it.
How agents work
The loop an agent runs:
1. **Observe** — Take in the current situation (your goal, any results from previous steps)
2. **Think** — Decide what action to take next
3. **Act** — Use a tool (search, write code, call API, etc.)
4. **Observe the result** — Did it work? What changed?
5. **Repeat** until the goal is complete
This loop is why agents can handle multi-step tasks that a single LLM call can't.
Real-world examples you can use now
Claude Code — Write a task in plain English. Claude reads your codebase, writes code, runs it, and fixes errors. It operates autonomously across multiple files.
Perplexity Deep Research — Give it a research question. It plans a research strategy, searches multiple sources, synthesizes findings, and produces a detailed report.
n8n AI workflows — Build automation agents that respond to triggers, process information with AI, and take actions across multiple apps.
Where agents work well
Agents are reliable when:
The task is well-defined with a clear success state
The tools they need are available and reliable
Errors are recoverable (they can try again)
Coding, research, data processing, and structured automation are current sweet spots.
Where agents still struggle
Agents are unreliable when:
The goal is ambiguous or keeps changing
They need to make high-stakes decisions without human review
They encounter unexpected situations their tools can't handle
The practical rule for 2025: use agents for defined, repeatable tasks. Keep a human in the loop for anything consequential.
Where this is going
Multi-agent systems — where multiple AI agents collaborate, each with different specializations — are the near-future direction. One agent plans, one researches, one writes, one reviews. This mirrors how human teams work and produces better results than a single agent.
Agents are AI with hands. They're most powerful for defined, repeatable tasks. Start with one use case, make it reliable, then expand.