An AI coding assistant is a software tool that uses AI models, usually large language models (LLMs) trained heavily on code, to help developers write, understand and change software. It typically runs inside the code editor or development platform, suggesting the next lines as a developer types, generating functions or tests from a description, explaining unfamiliar code and answering questions about a codebase. Some products go further and can make multi-file changes, run tests or prepare pull requests for review.
At a glance
- AI coding assistants suggest and generate code, explain it, write tests and documentation, and help find bugs.
- They usually work inside developers’ editors, command lines or code hosting platforms.
- Capabilities range from inline autocomplete to agent modes that plan and make larger changes with less supervision, depending on the product.
- Main risks are code leaving your control, insecure or incorrect suggestions, licence questions and unapproved use.
- Generated code still needs human review and your normal security testing.
What problem it solves
Developers spend much of their time on work that is necessary but repetitive: boilerplate code, tests, documentation, converting between formats, and reading unfamiliar code before changing it. Skilled developers are expensive and hard to hire, and backlogs of features and fixes keep growing.
AI coding assistants aim to cut the time spent on that routine work, help developers get up to speed on unfamiliar languages or legacy systems, and let teams spend more time on design and review. For organizations with a backlog of modernization work or technical debt, they are often pitched as a way to move faster without adding headcount. Results vary by team and task, so the benefit is something to measure, not assume.
How it works
Context gathering. The assistant collects context: the file being edited, nearby files, the developer’s request and, in many products, an index of the wider codebase or documentation.
Model request. That context goes to an AI model, hosted by the vendor, a cloud provider or, in some products, on your own infrastructure. The model returns suggested code, explanations or a plan of changes.
Interaction modes. Common modes are inline completion as the developer types, a chat panel for questions and requests, code review comments on pull requests, and agent modes that edit several files and run commands or tests. Agent modes share the risks of agentic AI, including being misdirected by prompt injection hidden in files, issues or web pages the tool reads.
Controls. Business tiers usually add administrator settings: which models are used, whether code is kept or used for training, filters that flag suggestions matching public code, usage reporting and single sign-on.
Review and testing. Suggestions go through the same pipeline as human code: peer review, static application security testing (SAST), software composition analysis (SCA) for dependencies, and automated tests in DevOps workflows.
When it matters for buyers
- When developers are already using them. Personal accounts on free tools are a common form of shadow AI; a sanctioned option with business terms is often safer than a ban that is hard to enforce.
- When source code is sensitive. Check where code is sent, how long it is kept and whether it trains models.
- When security and licensing matter. Make sure generated code passes through security testing and that you understand the vendor’s terms on ownership and indemnity.
- When measuring return. Compare cost per seat against measured changes in delivery speed and quality.
- When enabling agent features. Limit what agent modes can access, and keep human approval before merges and deployments.
Generated code should go through the same checks as any other code; see our application security testing options.
Questions to ask vendors
- Where is our code processed and stored, for how long, and is it used to train any model?
- Which models power the product, and can we choose or restrict them?
- What admin controls exist for policies, model choice, usage reporting and single sign-on?
- How do you flag suggestions that closely match public code, and what licence information do you provide?
- What intellectual property indemnity do you offer, and under what conditions?
- What can agent features do on their own, and how are their actions limited and logged?
- How is it priced: per seat, by usage, or both, and what happens when usage limits are reached?
- Can it be deployed or routed through our own cloud account or network if required?
How it differs from an AI assistant
A general AI assistant helps people with everyday knowledge work such as writing emails, summarizing documents and answering questions, often inside office software. An AI coding assistant is specialized for software development: it understands code and project structure, works inside developer tools, and connects to repositories and build pipelines. That specialization also brings its own risks, such as exposure of source code, insecure generated code and licence questions, which need controls a general assistant may not.