"AI coding assistant" now covers three different jobs wearing the same name: a completion engine that finishes your line, an in-editor agent that touches several files at once, and a headless session that runs a whole task unattended. Most buying guides treat them as one category and rank them on a single scale, usually speed or code quality. That scale answers a real question. It isn't the only one a software house needs answered before it standardizes on a tool.
What an AI coding assistant is
At its narrowest, an AI coding assistant is autocomplete with more context: it predicts the next few lines from what's already open, trained on public code and tuned to the current file. At its widest, the term now stretches to cover full agents, Claude Code, Cursor's agent mode, that read a repository, plan a change, edit multiple files, run tests, and report back. Both get called "AI coding assistant" in marketing copy and in search intent alike, which is why comparisons that don't first ask which job a tool is doing end up comparing things that aren't comparable.
Assistant, agent, and review tool are not the same job
Three categories cover most of what's on the market today:
Completion tools predict inside a single file, at the pace of typing. GitHub Copilot in its original form is the reference point: fast, low-friction, minimal context beyond the open buffer.
Agentic tools take a task description and work across the codebase: Claude Code from a terminal session, Cursor's agent mode from inside the editor. They read more, plan more, and produce a diff spanning several files instead of a completion inside one.
Review tools sit downstream of both: CodeRabbit, Greptile, and similar products don't write the change, they read a pull request and flag what a human reviewer might miss. Judging a review tool on how well it writes code is the wrong test; judging a completion tool on how well it reviews a pull request is the same mistake in the other direction.
A team that picks one category to solve a problem the other two are better suited for ends up disappointed with a tool that was never wrong for its actual job.
Criteria teams actually use today
Adoption gives a sense of where the money and the attention already are. Coding tools are the largest single category of departmental AI spend, close to $4B in 2025 and more than half of it, according to Menlo Ventures' 2025 State of Generative AI in the Enterprise, with adoption above 65% among top-quartile engineering teams. This isn't a niche tool category anymore; it's the default entry point for AI inside most engineering orgs.
What teams actually check before rolling a tool out to everyone, in rough order:
- Context window and codebase awareness. Can it reason across the repository, or only the open file.
- Interaction model. Interactive, block-by-block acceptance versus a task handed off and reviewed once finished.
- Where it runs. Editor-bound versus scriptable in CI, which decides whether it can run unattended at all.
- Review and merge fit. Does it produce a diff that fits the team's existing pull request workflow, or a new one to learn.
- Security and data handling. What leaves the network, and under what terms, especially across client codebases.
The 2025 DORA report adds a finding that most vendor comparisons leave out: AI adoption now correlates positively with throughput, epics completed per developer are up sharply, but it's still associated with more delivery instability, not less. The report's own framing is blunt: AI amplifies what a team already is. A strong workflow gets faster. A weak one gets faster at shipping the same problems. Picking the sharpest assistant doesn't fix that; it just runs the existing process at a higher speed.
The missing criterion: proof after the session
None of the five criteria above ask what a tool leaves behind once the task is done. That's not an oversight, it's outside what any of these categories are built to produce. A completion tool has no session to log. An agentic tool logs its own reasoning in its own format, in a place that doesn't travel if the team changes tools. A review tool comments on a diff but doesn't carry forward why the diff was requested in the first place.
For a single developer on a single repository, that gap rarely matters, the person who ran the session still remembers why. For a software house shipping into multiple client codebases, where the person who ran a task might not be the one answering for it three months later, "ask whoever ran the session" isn't an answer a client, an auditor, or a new hire on the account can act on. The criteria above tell you how fast a tool gets code written. They don't tell you what's left to show, after the fact, that the requirement, the authorization, and the release actually line up.
A decision table for software houses
| If the job is... | Reach for... | What it won't give you |
|---|---|---|
| Finishing lines inside a file, fast | A completion tool (Copilot-style) | Multi-file reasoning, any session log |
| An interactive multi-file change, watched step by step | An in-editor agent (Cursor) | Unattended, headless execution |
| A task handed off and run unattended, in CI or a pipeline | A headless agent (Claude Code) | A portable record if the tool changes |
| Catching what a human reviewer might miss in a PR | A review tool (CodeRabbit, Greptile) | Any account of why the change was authorized |
| Showing, months later, why a delivery happened the way it did | None of the above alone | — |
That last row is the one most buying guides skip, because none of these tools are built to fill it. Choosing an AI coding assistant means picking the right category for the job in front of you today. It doesn't settle the separate question of what a client, a reviewer, or a future hire can check once the session that produced the code is long over. That layer sits above the assistant, not inside it.