Before this:Using the provider's appAI inside your editorAgentic & command-line tools
App, IDE, agent — or all three?
Key takeaways No single right answer — the method should match the task, not the other way around. Five factors decide — task type, how much code the model needs to see, control versus speed, cost, and privacy. Most developers blend — the app for learning and planning, the editor for daily coding, an agent for big multi-file changes.
This is lesson 14 of the path, and it closes the module by tying the last three lessons together. You’ve now met the provider’s chat app, AI inside your editor, and agentic command-line tools. The natural question is “which one should I use?” — and the honest answer is that it depends on the task. By the end of this lesson you’ll have a clear way to decide, and you’ll see why the real answer for most people is “all three.”
There’s no single right answer
It’s tempting to look for the one best tool and use it for everything. But these three methods aren’t ranked from worse to better — they’re shaped for different jobs. An agent is not “the chat app but better”; it’s a heavier instrument that’s overkill for a quick question and indispensable for a multi-file refactor. The chat app isn’t “the beginner version”; it’s often the best tool for learning a concept, even for an expert.
So the useful framing isn’t “which tool do I prefer?” It’s “which tool fits this particular task?” That’s the same instinct a good developer brings to programming languages — there’s no single best language, just the right one for the problem (the language tour makes this point about languages, and it applies cleanly here).
The factors that decide
A handful of factors push a task toward one method or another. Run a task through these and the answer usually becomes obvious.
- Task type. Is it a quick question (what does this error mean?), an in-code edit (rename this, complete this function), or a whole feature (build and wire up something across files)? This is the strongest signal.
- How much code the model needs to see. A self-contained question needs almost no code. An in-file edit needs the current file. A feature might need the whole repo. The more the model needs to see, the further you move from the chat app toward editor and agentic tools.
- Control vs speed. The chat app and editor completions keep you firmly in control — you read and accept each piece. An agent trades some of that immediate control for speed across many steps. More control is safer; more autonomy is faster.
- Cost. Roughly, cost scales with how much the model does. A small pasted snippet is cheap; an agent looping over your repo and re-running tests consumes more tokens (see Understanding cost).
- Privacy. The more of your code a method touches, the more leaves your machine. A pasted snippet exposes little; an agent reading your whole repository exposes a lot. For sensitive code, lean toward methods that share less (see Security, privacy, and ethics).
Matching the task to the method
Put those factors together and a rough mapping falls out:
| Task | Code the model needs | Best-fit method | Why |
|---|---|---|---|
| “Explain this error or concept” | None / a pasted snippet | Chat app | Self-contained; you want understanding, not an edit |
| Plan an approach before coding | None yet | Chat app | No files involved; a conversation is the right shape |
| Complete a function as you type | The current file | Editor AI | Needs your open file; you want to stay in flow |
| Refactor or rename within a file | A selection | Editor AI | The change is local and you want to approve it directly |
| Build a feature across several files | The whole project | Agentic tool | Spans files and needs to run tests in a loop |
| Fix a batch of failing tests | The whole project | Agentic tool | Iterate-until-green is exactly the agent loop |
| Explore an unfamiliar repo | The whole project | Agentic tool (or editor chat) | Reading across many files to answer a question |
A GopherTrunk pass through the table: asking “why won’t this control-channel decoder lock?” as a concept question goes to the chat app; completing a table-driven test in ddc_test.go goes to editor AI; “add a new decoder mode and make make vet test pass” goes to an agent. Same project, three methods, each chosen by the task in front of you.
The realistic conclusion: blend
Here’s what actually happens in practice once you’ve used all three for a while: you stop choosing one and start blending them. A typical day might look like this:
- Open the chat app to think through a tricky design or learn an unfamiliar API — no code at risk, just understanding.
- Spend most of the day in your editor, letting completion and in-file refactoring carry the routine coding while you stay in control of each change.
- Hand a big, well-scoped, multi-file change to an agent, then review its diff carefully before keeping it.
These aren’t competitors. They’re three tools in one toolbox, and reaching fluidly between them — planning in the app, coding in the editor, delegating large changes to an agent — is what an experienced AI-assisted workflow looks like. The skill isn’t loyalty to one tool; it’s knowing which to pick up.
There’s also a learning curve worth respecting. It’s reasonable to start with just the chat app, add editor AI once that feels natural, and only reach for agents when you’re comfortable with the guardrails they need. You don’t have to adopt all three at once.
When you’re ready to assemble your own setup deliberately — picking methods, models, and habits that fit how you work — Module 6 walks through exactly that in Choosing your method. This lesson is the map; that one is where you draw your own route.
Quick check: What is the best general approach to choosing between the chat app, editor AI, and agents?
Recap
- No single right answer — the chat app, editor AI, and agents are shaped for different jobs, not ranked best to worst.
- Task type leads — a quick question, an in-code edit, and a whole feature each point to a different method.
- Code visibility — the more of your code the model needs to see, the further you move from the app toward editor and agentic tools.
- Control, cost, privacy — more autonomy means more speed but less direct control, higher cost, and more code shared.
- Blend in practice — the app for learning and planning, the editor for daily coding, an agent for big multi-file changes.
- Build it deliberately — Module 6’s Choosing your method helps you assemble your own setup.
Next up: zooming into the techniques themselves, starting with the one you’ll meet most often — letting the AI finish your code as you type. See Code completion.
Frequently asked questions
Which is best: the chat app, the editor, or an agent?
None is best in general — each wins for different tasks. The chat app is best for questions and planning, editor AI for daily in-code edits, and agents for big multi-file changes. The right question isn’t “which tool?” but “which tool for this task?”
Do I have to pick just one?
No, and most developers don’t. The common pattern is to blend them: the chat app for learning and planning, the editor for everyday coding, and an agent for large changes. Each is a different tool in the same toolbox, not competing products you must choose between.
How do cost and privacy factor into the choice?
Both scale roughly with how much the tool does. The chat app handling small pasted snippets is cheap and exposes little; an agent reading your whole repo and looping costs more tokens and sends more code to the provider. For sensitive code, prefer methods that share less, and check each tool’s data-handling policy.