AI coding assistants such as Claude Code or Codex write, change and test program code on instruction. They speed up routine work, but they do not replace planning, review and responsibility. For you as a client, that means certain steps go faster, while quality still depends on how carefully people check the result.
What are AI coding assistants and what kinds exist?
An AI coding assistant is a tool that connects a language model with a project's code. There are three levels. First, autocomplete in the editor, which suggests lines. Second, chat in the editor, in which you ask questions about the code or request changes. Third, agentic tools such as Claude Code or Codex, which read the code, run commands, start tests and change several files. How such agents work in general is explained in What is agentic AI?.
What changes for software projects?
The table shows how typical tasks change and what people continue to do.
| Task | Effect of AI | What people still do |
|---|---|---|
| Recurring code, forms, interfaces | Drafted much faster | Clarify requirements, check the result |
| Writing tests | First versions quickly | Decide what needs testing |
| Refactoring and tidying | Many places in a short time | Assess risks, test the result |
| Bug hunting | Helpful with clear error messages | Understand causes, check side effects |
| Architecture and security | Suggestions, not a decision | Bear responsibility |
| Documentation | Good first drafts | Ensure correctness |
The findings on time savings differ. An experiment on GitHub Copilot found that developers solved a clearly defined task faster with the assistant (Peng et al., 2023). A study by METR (2025), by contrast, found that experienced developers working in their own mature projects took longer with the tools, although they had expected the opposite. So the benefit depends on task, experience and way of working.
For your budget, this means: the time for routine code falls, but the time for clarifying, designing, testing and acceptance does not fall to the same degree. On small projects this produces faster prototypes; on large ones the emphasis shifts to planning and review. Ask for a traceable plan rather than trusting blanket promises such as "half the price".
What are the risks?
Generated code can contain bugs and security holes, use invented library names or build in logic that only appears to work. Because changes arise quickly, more unchecked code can arise too. Then there are data protection questions: code and project data often go to the model provider.
The remedies are well known: automated tests, review by a second person or a second tool, security checks, clear permissions and logs. We use such tools ourselves and check results through tests, code review and a visual check in the browser before anything goes live. How humans and machines work together is described in What is human in the loop?.
What should you ask your agency or team?
Ask five questions. Do you use AI tools, and for which steps? Who reviews the code, and which automated tests exist? Which data goes to AI providers, and how is that secured by contract? Who owns the result, and how is it documented? How does the use affect timeline and price?
- Use of AI tools and affected steps asked about
- Code review and automated tests named
- Data flow to AI providers and data processing agreement clarified
- Ownership and documentation of the code settled by contract
- Effect on timeline and price discussed
- Acceptance with real test cases agreed
Conclusion: faster, but not without review
AI assistants change the route to the result, not the responsibility for it. Use the gain in speed where it is safe and insist on tests and review. If you want to know how we deliver software and automation with AI support, see our AI automation service or describe your project.
