The term "AI agent" has become common, and so has the confusion around it. A lot of tools call themselves "agents" when they're really just autocomplete with a chat interface. This isn't just a marketing word — the distinction between completer and agent is what decides whether the tool can build an app, or only help you write a line.
AI completer vs AI agent
An AI completer suggests the next line or block as you type. It sees what you wrote and guesses what comes next. It's passive: you write, it suggests. It doesn't know what the app is supposed to do beyond the current file.
An AI agent is active. It plans: from the requirement description, it decides which files to touch, in what order. It generates: it writes whole code, not just completions. It runs and validates: it tries the code — runs a build, runs tests, checks whether it works. And it iterates: if it doesn't work, it reads the error and fixes it. It does the work, not just suggests text.
This distinction isn't theoretical. A completer can't build an app by itself; it can only speed up your code-writing. An agent can take "build me an order-management system" and realize it. That's the difference between a code assistant and an app generator.
How an agent builds an app
The typical flow, at a conceptual level, is: the agent reads the project — which files exist, what the data model is, what the API contract is. It plans the feature: which files to touch, which to create new, what the dependencies are. It generates the code. It runs it — tries to build, tries to run, tries to test. If there's an error, it reads it, fixes it, and repeats. That loop — plan, generate, validate, fix — is the core.
The details of how each tool implements this vary, and we won't go into them here — there's no value in describing specific mechanisms that could be wrong or change. The important idea is that an agent is not just a language model; it's a language model inside a loop, with access to the code and the results of running it. The loop is what makes it a builder, not just a text composer.
The context cliff
Here comes the problem that separates an agent that works from an agent that demos and dies. In most tools, every new session starts with a fresh rescan of the files to build context. This works for a small project. But as the project grows — more files, more entities, more dependencies — the model's context window can't hold it all. The agent gets a partial picture, and starts guessing.
The symptoms are familiar: the agent rewrites code that already worked, because it didn't know it existed. It generates endpoints that collide with previous ones, because it didn't see them. It asks you questions you already answered in a previous session, because the context was lost. This is the "v10 problem" — the tool is great for v1, and falls apart when the project reaches v10. <a href='blog-context-loss.html'>We wrote about this separately in the post on context loss</a>.
The key point: this isn't a model-quality problem. A smarter model will guess better, but it will still guess. The problem is architectural — it stems from the decision to build context fresh each session from a file scan, instead of keeping a persistent model of the project. No model solves this; only a different architecture does.
Why persistent context is the fix
The fix isn't to stuff more into the context window; that only postpones the problem. The fix is that the agent keeps a durable model of the project — the entity structure, the API contract, the relationships between files — that persists across sessions. When you open a new session, the agent doesn't start from zero. It knows what it built, what decisions were already made, what the current contract is.
This doesn't mean the agent doesn't read code. It does — but it reads from understanding, not from blind search. That understanding doesn't reset each session. It accumulates. And as the project grows, that accumulated understanding is what lets the agent keep working without forgetting itself.
What this means in practice
The practical distinction is simple: an agent that remembers is a daily-work tool. An agent that forgets is a demo tool. If you're building a project that will live longer than a day, the context cliff will arrive; the only question is when, and whether your tool handles it or falls off it.
This is why Vibe Coder is built around persistent context. Not as a nice feature, but as an architectural principle: the project's understanding is an asset that's preserved, not something rebuilt every time. <a href='ai-coding-agent.html'>More on our conceptual model of an AI coding agent is on the concept page</a>.