What are AI agents - and why everyone talks about them now

You’ve probably seen terms like AI agents, Codex, Gemini, Claude, or vibe coding - and thought: okay… what does this actually mean?

In practice, an AI agent is software that can interact with the data you provide to it, then decide what action to take next using an AI model.

You interact with an agent through a chat UI: you give instructions, and it reads context (your data) and performs actions. You can either use an existing agent and guide it step by step, manually, or build your own agent as a workflow with predefined steps - where some steps use AI to generate content or make decisions.

In this article, you’ll learn how agents work, what “vibe coding” actually means, how to use AI safely (including securing AI-generated code), and how agents are changing developer teams.

Where you’ll meet them

Here are common AI agent use cases:

1. Inside a code editor

This is the classic developer setup: VS Code, JetBrains, and similar code editors.

As a plugin

You install a plugin, you get a chat panel, and you start giving instructions to an agent. The agent can interact with your project’s filesystem:

• read and review your project files
• search through the codebase
• generate new code
• edit files
• refactor, rename, reorganize
• run commands
• and iterate: change → check → fix → repeat

So it’s not only answering questions - it’s interacting with your actual project.

As a standalone editor (for example, Cursor)

Cursor is a code editor built on top of VS Code, with AI features integrated by default. So when you install Cursor, you already have everything prepared to use AI for writing and analyzing code.

The workflow becomes simple: you focus on what to build, and the editor + agent helps with how to build it.

This unlocks a new level of productivity. Instead of writing every line by hand, you shift toward managing tasks and refining logic.

2. In everyday work tools

AI agents can help you automate routine tasks, for example:

• summarize YouTube videos
• summarize documents, financial reports, books, articles, etc.
• control your browser (search websites, fill forms, book tickets)
• draft replies and fix your grammar
• time management (plan your day, prioritize tasks, reminders)
• pronunciation checker (practice speaking and get corrections)
• text-to-speech voiceovers (turn text into audio)
• auto-response chatbots (reply to customers)
• record meetings and summarize key points
• turn notes into tasks and checklists
• generate websites
• generate images, slides, and videos
• generate diagrams, charts, and other data visuals
• and this list is constantly expanding

It’s the same concept: you give a task in plain language, and the tool can do work - not just chat.

What’s inside an agent

When people say “AI agent” they usually mean: there is some AI model behind it (the brain, like gpt-5.2 or claude-sonnet-4.5 or gemini-2.5-flash), and the agent is the tool that wraps it (the interface + the automation).

An agent works like a loop around the AI model.

It starts with your message, then the agent adds hidden instructions (a “system” layer) that describe what this agent is designed for and what it is allowed to do. Those hidden instructions can also define tool commands - for example: “if you need to read a file, request the tool read_file filename.”

Then the agent executes the tool call, gets the result (like file contents), and sends a new request to the AI model - now with that extra context included. This repeats in a cycle (message → tool call → result → next request) until the agent decides the task is finished.

Custom agents for business

You can also build your own AI agents tailored to your business needs.

In simple terms, you take a “raw brain” (an AI model) and wrap it with your own instructions and step-by-step workflow: where to read data from, what to generate, and what actions to take.

Tools like n8n make this approachable: you can build an automation flow, add AI steps (summarize, classify, draft), connect your apps (docs, CRM, helpdesk), and run it continuously.

Why it’s moving so fast

Because the competition is very strong.

Most tech companies are building agents internally or shipping them as products, because it’s a direct advantage:

• faster work
• less manual routine
• easier scaling
• better automation

That’s why you see frequent model releases and “new agent features” all the time. Everyone wants to be the default tool you use to do work.

Vibe coding, explained

This phrase appeared because the workflow changed.

Old flow:

• you write everything manually
• you debug everything manually
• you slowly move step by step

New flow (vibe coding):

• you describe what you want
• AI generates / modifies code
• you run / test / profile
• you adjust instructions
• repeat

So your skills still matter - you’re just applying them differently: less typing, more directing and verifying.

Now the key skill becomes:

• knowing what to build
• knowing how to verify it
• being able to guide the agent with good context and constraints

This is why “prompting” (or “prompt engineering”) became a thing. Because if you can’t describe the task clearly, you’ll get random output.

Good vibe coding is: AI code generation + your ability to direct it + your ability to verify it

If you skip the verify part, you’re not “moving fast”. You’re just taking a risk.

What changes for teams

When everyone can generate “code that currently works”, two things happen:

1. More code is shipped (a lot more)
2. Manual review becomes a bottleneck

It becomes harder to:

• keep consistent architecture
• keep quality stable
• keep security under control
• understand if a person actually knows what’s happening
• onboard people without turning codebase into a zoo

That’s why the next layer becomes important: automated review and security tooling.

Tools like:

• Snyk (security scanning / vulnerabilities)
• CodeRabbit (PR review, logic issues, suggestions)

help protect the codebase from vibe coding side effects and reduce manual review load.

So yes, it’s a real shift - and it changes how teams organize.

But it’s still programming. The fundamentals don’t go away - they get amplified: clear requirements, constraints, tests, and review.

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AI agents can be a life-changing productivity boost - but only if you put in the effort to protect your workflow: set constraints, verify outputs, and treat hallucinations as a normal risk (not a rare exception). The other “new skill” is learning how to communicate with the agent - prompting - and getting better at it over time.

In the next articles, I’ll share practical prompt-engineering patterns, checklists, and guardrails that help you ship faster without turning your codebase into chaos.