An AI coding assistant is any tool that writes or edits code on your behalf, whether it works as you type in the editor, in a chat panel beside your files, or as an agent that edits a repository and runs the test suite. By 2026 the category has split into four product shapes that differ far more than their marketing pages suggest. The practical question is not which one is best but which fits the way your team already reviews and ships code. This guide covers what each shape is good for, the checks that separate a tool that survives a real codebase from one that does not, and a four-week pilot that keeps the blast radius small.
Feature lists blur the difference, so start by identifying which of these you actually need.
Most teams can trial two candidates in a fortnight. Compare these dimensions on real work rather than on a demo.
| Dimension | What to check | Why it matters |
|---|---|---|
| Editor and language fit | Native support for your IDE and main languages, not just the popular ones | A tool that needs a workaround on your main editor gets closed after a week |
| Repository context | How much of the project it can read, and whether it indexes locally or in the cloud | Suggestions that ignore your own helpers produce unusable code |
| Permission model | What it can edit, run and push, and where approval is required | An agent with shell access and no gate is an incident waiting to happen |
| Data handling | Processing location, retention period, training opt-out, ignore-file support | Determines whether you can use it on client or regulated code |
| Enterprise controls | SSO, seat management, audit logs, policy controls, self-hosted option | Needed before a team-wide rollout, not after |
| Price and usage caps | Seats, included usage, what happens past the cap, and whether agents burn more | Agentic features consume several times the tokens of completion |
A two-week trial on one repository tells you more than a month of evaluation slides. Ask the people who review the pull requests, not only the people who write them.
Code leaves your machine the moment a cloud assistant reads it, so the data terms are part of the engineering decision. Check where prompts and repository context are processed, how long they are kept, and whether your code can be used to train a model. Enterprise tiers usually add a no-training guarantee, retention limits and audit logs; consumer tiers often do not. If a client contract forbids third-party processing, that repository stays out of the tool, or the team runs a self-hosted model instead.
Secrets are the more practical risk. An agent that can read your workspace can read the environment file, the deployment token and the local config. Keep credentials out of any directory the tool indexes, verify that it honours your ignore files, and audit the keys that already sit in the repository.
Licensing is the third. Some tools filter suggestions that match public code, some do not, and the filter is a setting rather than a guarantee. If your company has an open-source policy, confirm how the assistant handles matches and keep that setting on. Reviewing a diff for provenance is far cheaper than dealing with a licence question after release.
The integration pattern is the same one that applies to any AI step in a workflow: the tool drafts, the human judges, and the loop is measured rather than assumed.
If your team is still deciding how AI tools fit into the wider delivery process, the same evaluation logic applies to other categories, from meeting capture to content operations. The rule that holds across all of them: pick for fit, pilot on real work, and keep a human at the decision point. That is the way we approach tool selection at Cactus Tech AI, and it is covered in our service overview.
An assistant suggests code - a completion, a snippet or an explanation - and you decide what to do with it. An agent goes further: it edits files across a repository, runs commands or tests, and iterates on the result. Agents save more time on mechanical work and need clearer limits, so review the diff, restrict what they can execute, and keep a human checkpoint before anything merges.
Start from your editor and your main language rather than from a feature list, then run two candidates on the same repository for two weeks. The tool whose suggestions your team actually accepts, and whose review burden is lowest, is the better choice regardless of what a comparison table says.
It depends on the data terms of the vendor and on your own obligations. Check where code is processed, whether it is retained, and whether your work can be used to train models. Enterprise plans usually add controls such as no-training guarantees, retention limits and audit logs. If client contracts forbid third-party processing, keep the tool out of those repositories or use a self-hosted option.
Treat it as a fast junior contributor: useful, sometimes wrong, and in need of review. Generated code frequently calls plausible but non-existent APIs, mishandles edge cases, and repeats patterns from outdated versions. Keep tests and code review in the loop, and do not let an assistant commit straight to a protected branch.
Four weeks is a reasonable minimum. One week to choose and set up, two weeks of real work on a single repository, and one week to compare the numbers and write a short usage policy. A longer pilot rarely changes the decision; what changes it is whether reviewers notice a difference in quality.
References - official product documentation (checked September 2026)
Last updated: 2026-09-13. Assistant features, data terms and pricing change quickly; verify current details on the official pages above before standardising on a tool.