AI agents are changing software development: Cursor and Claude Code vs. the skeptics

AI agents in software development provoke polarized opinions in 2026. Some see them as the next step after Copilot and a way to close routine tasks much faster. Others fear technical debt, security problems and yet another wave of inflated expectations around artificial intelligence.

What the agentic approach to development means

An AI agent in development is an autonomous system that does not merely suggest code autocompletion, but understands the whole task and carries it out step by step. The key difference from Copilot is that an agent can do all of the following at once:

• Analyze the entire codebase, not just individual lines
• Make decisions on its own: 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, a compiler, the IDE

This is fundamentally closer to "turn on the agent and forget about it", although practice shows it is too early to forget.

Cursor and Claude Code: the main players of 2026

Cursor positions itself as VS Code with AI superpowers. Its built-in agent can autocomplete code, generate functions from scratch and refactor. Users note that the tool handles routine tasks well: adding a function to the end of a file, creating boilerplate, renaming a variable across a project.

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

• The agent sees the structure of the entire project at once
• It can change several files in parallel
• It runs tests and analyzes the results
• It rolls back automatically on error
• It can work with git: create branches and make commits

On well-structured, mid-sized projects (a few thousand lines of code), Claude Code shows impressive results.

Where the problems begin

Critics rightly point to real pitfalls of the agentic approach:

• Technical debt keeps growing. An agent generates working code, but rarely optimal code. Over time, that code has to be redone.
• Hallucinations, delivered with confidence. AI can "confidently" write a function with a logic error that is easy to miss in review.
• Security problems. If you give an agent full access to a project, it may accidentally leak sensitive data.
• It does not solve the main problem. The hardest part of development is architecture design and strategic decisions. AI does not do that yet.

What this means

AI agents are a powerful tool for speeding up routine work, but not a replacement for engineering judgment. In 2026, a sensible approach is to use them for real but non-critical tasks (writing tests, database migrations, refactoring), while keeping a human in the loop and not entrusting them with the fate of the architecture.

The agentic approach works if the team includes people who understand what the agent is doing.