A client of mine was on a call with a contractor last month, and the contractor said something like, "we're running this through Claude Code overnight, then Cursor will handle the polish, and if we hit something weird I'll just paste it into a chat." My client nodded along and then texted me afterward: what did any of that mean, and should he be worried he just approved something he didn't understand? That's a fair question, and it comes up constantly. You don't need to become technical to run a business that uses AI-assisted development. But you do need to know that these tools aren't one thing wearing different logos — they're three genuinely different shapes of tool, built for different jobs, and the difference actually matters to you as the person paying for the work.

Here's the plain version. There are terminal-based coding agents that work autonomously across your whole codebase. There are assistants built into the code editor that suggest code as a developer types, line by line. And there are chat tools where a developer copies code back and forth by hand. Same broad goal — get code written faster and with fewer mistakes — completely different level of independence, completely different risk profile, completely different reason to pick one over another for a given task. None of them is "the best one." They're not really competing for the same job.

Start with the terminal agents — things like Claude Code, OpenAI's Codex, and a growing list of similar tools that work the same way. You give one of these a task in plain English — "migrate our billing logic to the new pricing tiers" or "find why checkout is failing intermittently and fix it" — and it goes and does the work mostly on its own. It reads through your actual codebase, not just the one file someone has open. It decides which files need to change. It can run commands, execute tests, check its own work, and come back with a result, sometimes touching dozens of files in a single pass. This is the category people mean when they say "AI agent" instead of "AI assistant." The distinction is real: an assistant helps you do something, an agent goes and does the something.

That autonomy is the whole point, and it's also exactly where you should be paying attention. A tool that can independently rewrite forty files overnight is enormously useful — it's the difference between a task taking a week and taking an afternoon. But it also means a mistake doesn't happen in one place, it can happen in forty places at once, and it can happen while nobody's watching. This is not a reason to be scared of these tools. It's a reason to ask your contractor or technical hire a very specific question: what's the review step? A competent developer using an autonomous coding agent is still looking at every diff before it ships, still running it against a test environment before production, still using version control so any change can be rolled back. If the answer to "who checked this before it went live" is a shrug, that's the actual problem — not the tool.

Second category: assistants that live inside the code editor and suggest code as someone types, the Copilot-style tools. These don't go off and do a task on their own. A developer is sitting there writing code, and as they type, the tool predicts what comes next — finishing a line, suggesting a whole function, catching a pattern it's seen before — and the developer accepts, edits, or ignores the suggestion in real time. The human is doing the work; the tool is a very fast, very well-read typing partner. This is the lowest-risk category of the three in terms of scope, because nothing happens without a person actively sitting there approving it keystroke by keystroke. It's also the category best suited to routine, repetitive coding — the boilerplate, the standard patterns — rather than big structural decisions about how your systems should work. One thing worth knowing: a tool like Cursor lives in this category through its line-by-line suggestion feature, but it — like a few others in this space — has also added its own more autonomous, multi-file agent mode that behaves a lot more like the first category. So this is really a description of how a tool is being used in a given moment, not a permanent label stamped on one product forever.

Third category, and the one that's been around the longest in spirit: chat-based tools where a developer copies a piece of code or an error message into a conversation, gets back an explanation or a suggested fix, and manually pastes it into their own project. No autonomous access to your codebase. No ability to run anything on its own. Everything moves through a human's copy-and-paste. This is the slowest of the three, and also the easiest to audit, because every single change passed through a person's hands and eyes on the way in and the way out. For a quick one-off question — "why is this error happening" — it's often exactly the right amount of tool. Nobody needs an autonomous agent let loose on your codebase to explain a stack trace.

Why should you, a business owner with no engineers reporting to you directly, care about any of this? Because when your team tells you what they're using, they're actually telling you two things at once: how much independent action they're comfortable letting AI take on your systems, and how much oversight is actually happening around that action. "I had the agent handle the whole migration" is a very different sentence from "I pasted the error into a chat and fixed it myself" — not better or worse, just a different scope of autonomy and a different set of review practices that should be sitting behind it. The tool name is shorthand for a workflow. Learning to hear that shorthand is the actual skill here, not memorizing product names.

So the useful question isn't "which one should we use." It's "which one fits this job, and what does using it responsibly look like." A large-scale change across your whole system is a plausible candidate for an autonomous terminal agent, with real review built around it. Day-to-day feature work benefits from the inline, in-editor kind, because it keeps a person in the loop on every line. A quick diagnostic question is often perfectly well served by a chat tool and five minutes. If a contractor is using a heavyweight autonomous agent to answer what should have been a two-minute question, that's worth asking about — not because the tool is bad, but because it may signal they're not thinking about fit, or worse, that it's a costlier tool and you're the one covering the difference.

I'll say the unglamorous part plainly, because I don't sell hype and I'm not going to start now: none of these tools remove the need for a person who understands your business, your data, and your risk tolerance. They all still make mistakes — sometimes confident, plausible-looking mistakes that are wrong in ways a non-technical eye won't catch. What they change is how much a competent developer can get through in a day, not whether you still need a competent developer exercising judgment. If someone pitches you on any of these as a replacement for having a technical person think about your systems, that's the point where I'd get skeptical, not enthusiastic.

None of this requires you to become a developer. It requires you to ask a few plain questions when these names come up — what's this tool actually doing, who's checking its work, and does the amount of autonomy it has match how much trust the task deserves. That's a business question, not a technical one, and you're already equipped to ask it.

I write about this kind of thing — cutting through the noise around AI tools for people who don't have an engineering team to translate it for them — at 013labs.com.