AI agents are changing development: Cursor and Claude Code against the skeptics

AI agents in software development are causing polar opinions in 2026. Some see them as the next step after Copilot and a way to close routine tasks significantly faster. Others fear technical debt, security issues, and another wave of inflated expectations around artificial intelligence.

What is the agentic approach in development

An AI agent in development is an autonomous system that doesn't just suggest code auto-completion, but understands the entire task and executes it step by step. The key difference from Copilot is that an agent can simultaneously:

• Analyze an entire code project, not just individual lines
• Make decisions independently: open a file, read documentation, run a test
• Fix errors on the fly if a test fails on the first attempt
• Work for hours on a complex task without interruption
• Use external tools: git, test frameworks, compiler, IDE

This is fundamentally closer to "launch the agent and forget" — though practice shows it's too early to forget.

Cursor and Claude Code: the main players of 2026

Cursor positions itself as VS Code with AI superpowers. The built-in agent can auto-complete code, generate functions from scratch, conduct refactoring. Users note that the tool works well on routine tasks: add a function to the end of a file, create boilerplate, rename a variable across the project.

Claude Code is a more ambitious approach. It's not an IDE, but a mode of operation in Claude's text interface, where the agent gets direct access to the file system and terminal. In practice this means:

• The agent sees the entire project structure at once
• Can modify multiple files in parallel
• Runs tests and analyzes results
• Rolls back automatically on error
• Can work with git: create branches, make commits

On well-structured medium-sized projects (thousands of lines of code), Claude Code shows impressive results.

Where the problems begin

Critics rightly point out real pitfalls of the agentic approach:

• Technical debt spirals. The agent generates working code, but rarely optimal. Over time, such code needs to be redone.
• Hallucinations and confidence in them. AI can "confidently" write a function with a logical error that's easy to miss during review.
• Security issues. If you give the agent full access to the project, it can accidentally leak sensitive data.
• Doesn't solve the main thing. The hardest part of development is architecture design and strategic decisions. AI can't do this yet.

What this means

AI agents are a powerful tool for accelerating routine work, but not a replacement for engineering thinking. In 2026, the sensible approach is to use them for practical but non-critical tasks (writing tests, database migrations, refactoring), but keep humans informed and not entrust the fate of architecture to them. The agentic approach works if the team has people who understand what the agent is doing.