Chat, skills, agents and coding agents: what's the difference?

Businesses use AI in four main ways. Chat is a conversation you drive, one question at a time. A skill is packaged know-how that teaches the AI to do one task consistently. An agent works toward a goal on its own, using your tools and taking actions. A coding agent is an agent built to write, run and fix software. Each step adds capability, and each needs more care.

The names get blurred in product marketing, so this guide uses plain definitions and marketing examples. It's the first part of our AI 101 series; if you want the basics of how the models themselves work, start with how AI language models work.

Chat: a conversation you drive

Chat is what most people mean by AI: ChatGPT, Claude, Gemini or Microsoft Copilot in a browser or app. You type a request, the model replies, and you steer with follow-up messages. Everything happens because a person asked, and nothing leaves the chat unless a person copies it out.

Chat is excellent for thinking and drafting: outlining a campaign, rewriting a paragraph for a different audience, summarizing a long report you paste in, or brainstorming subject lines. It's also where every team should start, because it's cheap, low-risk and teaches people what the models do well and badly.

Its limits come from the same design. Each new chat starts from scratch, so you re-explain your brand, your audience and your standards every time. Quality depends on how well the person asks, which is why good prompts matter (see how to write AI prompts that work). And chat can't act: it can draft the email, but it can't check your CRM, schedule the send or notice when a campaign breaks.

A step up: projects and custom instructions

Most assistants now let you save instructions and files for a recurring job: projects in Claude and ChatGPT, custom GPTs, and Gems in Gemini. A project that holds your brand guide, product facts and tone rules removes a lot of repetition. It's still chat, though: a person starts every conversation and moves the results where they need to go.

Skills: packaged know-how, loaded when needed

A skill turns a repeatable task into something the AI can pick up and do the same way every time. Anthropic introduced Skills for Claude in 2025: a skill is a folder with a SKILL.md file of instructions, plus any reference files and scripts the task needs. Claude reads the short description of each skill it has, and loads the full instructions only when a request matches. Similar ideas are spreading across other AI and agent tools under names like skills, instructions and playbooks.

The difference from a saved prompt is depth and consistency. A good skill includes the checklist, the rules, the output format and sometimes a small program that does part of the job precisely. For example, our free Campaign QA agent for Claude is a skill: it carries a written QA checklist, a UTM naming convention, a report template and a script that checks every link the same way. Anyone on the team gets the same review, whether it's their first campaign or their fiftieth.

Skills are still triggered by a person in a conversation. They make chat far more reliable for a defined task, but they don't watch your systems or act on a schedule. That's where agents come in, and agents can use skills too.

Agents: AI that works toward a goal and takes action

An AI agent is software that works toward a goal with a degree of independence. It starts from a trigger (a form fill, a schedule, a new record, a phone call), gathers the context it needs, decides the next step, uses tools such as your CRM, analytics or email platform, and records what it did. The key differences from chat are that it doesn't wait for a person to type each step, and it can change things in other systems.

Agents connect to those systems through APIs and, increasingly, through the Model Context Protocol (MCP), an open standard Anthropic introduced in 2024 that lets AI applications use tools and data sources through a common interface. In practice, that means an agent can look up a contact, check a calendar or pull last week's numbers without a person copying data back and forth.

Marketing examples include an agent that qualifies inbound leads and books meetings, one that writes the weekly performance summary from GA4 and ad data, and one that answers the phone as an AI receptionist. Several agents can also work together: in our ATL Crew, one agent answers leads, one tracks results and one launches campaigns, handing work to each other through the CRM.

Because agents act, they need guardrails that chat doesn't: access limited to what the job needs, a person approving anything customer-facing, costly or irreversible, a log of every action, and a test set of real examples run before launch and after every change. An agent without those is a liability, however clever the model.

Coding agents: agents that write and run software

A coding agent is an agent specialized for software work. Tools such as Claude Code, OpenAI Codex and GitHub Copilot's coding agent can read a codebase, plan a change, edit files, run commands and tests, and propose the result for review, often as a pull request. They work in a code environment rather than in your marketing tools, and they're given a goal (“fix the broken form tracking on the pricing page”) rather than a single question.

For marketing teams, coding agents are useful for work that used to wait in a developer's queue: building or updating landing pages, fixing tracking and data layer issues, writing small scripts to clean exports or check links, and creating internal tools. They're also how many agents and skills get built in the first place.

The same rule applies as for any developer's work: review before it ships. A coding agent can produce a lot of plausible code quickly, including code with security or accessibility problems. Treat its output as a strong first draft that a person who understands the system checks, tests and approves.

How to choose the right one

Start with the task, not the tool. The table below matches common situations to the lightest option that will do the job well.

One marketing task, four ways

Consider a hypothetical team that sends a monthly performance report to leadership.

• With chat, an analyst exports the data, pastes it in, asks for a summary, checks the numbers and copies the result into an email. It saves writing time, and it takes the same effort next month.
• With a skill, the analyst attaches the exports and says “write this month's report”. The skill applies the same structure, the same metric definitions and the same data-quality checks every time.
• With an agent, the report is drafted automatically on the first business day from the connected analytics and CRM data, and an analyst reviews and approves it before it's sent.
• With a coding agent, a developer asks for a small tool that pulls the data and builds the charts, reviews the code and schedules it, and the agent or the team uses that tool from then on.

Each version is legitimate. The right one depends on how often the task happens, how much it matters when it's wrong, and whether your data is ready to connect. Our guide to choosing AI tools for your marketing team covers the practical selection questions, and an AI usage policy for marketing teams covers the rules that keep each level safe.

Where to start

If your team is new to AI, get comfortable with chat on real work, then turn your most repeated task into a skill. When you find work that spans several systems and happens on a schedule, that's a candidate for an agent. ATL Martech designs and builds AI agents for marketing, sales and analytics teams, starting with a short workshop that sorts your workflows into those levels, so you only build what will pay for itself.