Coding with AI: how Claude Code can change the way you build software

For years we used AI to generate code snippets, explain errors or answer questions in a chat. Claude Code proposes something different: working with AI directly on your project, from the terminal. It analyzes files, understands the repository structure, investigates problems, modifies code, runs commands and validates the changes. AI stops being a tool for writing code and starts taking part in the development work.

What Claude Code is

Claude Code is Anthropic's tool for working on software projects from the development environment. Instead of asking it "how do I fix this error?", you give it the real context of the project and ask it to investigate.

Investigate why this endpoint returns a 500. Review the related code, identify the cause, propose a fix and run the corresponding tests.

The fundamental difference: Claude Code works with the real context of the repository, not with the snippet you pasted into a chat. If your role is more about leadership than code, we have a dedicated guide on enterprise adoption in the related articles.

What it can do on a real project

Depending on the permissions you grant it, it can help you:

• Analyze the architecture and find where an error originates
• Modify code across multiple files in a coordinated way
• Build new features and refactor components
• Run tests, review logs and work with Git
• Generate documentation and investigate configuration problems
• Automate repetitive development tasks (migrations, cleanups, releases)

From generating code to solving problems

The important change is not that it writes code — many tools already do that. The change is being able to hand it a complete problem:

A user reports they can't recover their password. Investigate the full flow: frontend → API → service → database → email. Determine where the problem is, implement the change and run the tests.

The AI stops receiving a question and receives a task. In a business project there is no single file that explains the problem: there's backend, frontend, database, APIs, Docker, environment variables, logs, tests and external services. A tool that only generates code has limited information; one that can investigate the whole project works with the same context as a developer.

Technical responsibility is still human

Claude Code can execute actions on your project, but that doesn't remove technical review. In real projects you need to control:

• Permissions and access to secrets
• Destructive changes and database modifications
• Dependencies and security
• Tests and architecture changes

AI speeds up the work. Responsibility still belongs to the team. Our rule at WITS: the agent proposes and executes in controlled environments; a person approves the final diff.

How we use it at WITS: a concrete example

The visual redesign of this very site (wits.mx) was done in a single working session with Claude Code: a full color palette migration across 45 points in the code, a new hero with video, cleanup of 8 MB of dead assets, 4 commits and a verified production deploy the same day. Work that normally takes a frontend developer two or three days was done in hours — with human review of every change before the deploy.

Who is it for?

• Developers: investigating problems, refactoring and automating repetitive tasks
• Development teams: speeding up analysis that eats up hours
• Software companies: improving productivity without growing the team proportionally
• Teams maintaining large systems, where understanding the existing code is the biggest cost

If you build software, the best way to understand the difference is to use it on a real project.

Beyond the tool

At WITS we work on this evolution: AI + software + automation + integration. It's not about adding AI because it's trendy, but about identifying where it produces a measurable result — reducing manual work, speeding up processes, improving sales, cutting costs and empowering teams. Claude Code is a good example of where applied AI work is heading.