AI Coding Agents: How AI Writes and Reviews Code

AI coding agents represent one of the most mature and impactful applications of agentic AI. Unlike code completion tools that suggest the next few tokens, coding agents can understand entire codebases, plan multi-file changes, write implementations, run tests, debug failures, and iterate until the code works correctly. Tools like GitHub Copilot Workspace, Cursor, and Claude Code have demonstrated that AI can handle substantial software engineering tasks, from bug fixes to feature implementations, with increasing reliability.

How Coding Agents Understand Code

Before an agent can modify code, it must understand the existing codebase. This understanding happens through several mechanisms:

Codebase indexing creates a searchable representation of the project. File structures, function definitions, class hierarchies, and import relationships are mapped to enable targeted navigation. When the agent needs to understand how a particular function works, it can quickly locate its definition and all its callers.

Semantic code search goes beyond filename and symbol matching. The agent can search for concepts like "the function that handles user authentication" and find relevant code even if the naming conventions are inconsistent. This uses embedding-based search over code combined with language understanding.

Context gathering assembles the relevant code files, documentation, tests, and configuration needed to understand and modify a specific part of the codebase. This is analogous to how a human developer would open related files before making changes.

A coding agent's effectiveness is directly proportional to how well it understands the codebase. Agents that can navigate large codebases and understand architectural patterns produce better, more consistent code than those working with limited context.

The Code Generation Loop

Coding agents follow an iterative loop for implementing changes:

1. Plan: Analyze the task, identify which files need to change, and determine the order of modifications
2. Implement: Write or modify code across one or more files
3. Validate: Run linters, type checkers, and tests to verify the changes are correct
4. Debug: If validation fails, analyze the error, understand the root cause, and fix it
5. Iterate: Repeat steps 2-4 until all checks pass

This validation-driven development loop is what makes coding agents significantly more reliable than one-shot code generation. The agent does not just generate code and hope it works; it verifies its work and fixes issues, just as a human developer would.

Test-Driven Development with Agents

Some coding agents can write tests first and then implement code to make them pass, following test-driven development principles. This approach is particularly effective because the tests serve as a concrete specification of what the code should do, and the agent has a clear, automated way to verify its implementation.

Code Review Agents

Beyond writing code, agents excel at reviewing it. Code review agents analyze pull requests and provide feedback on correctness (logical errors, off-by-one bugs, null pointer risks), style and consistency (adherence to project conventions and best practices), security (potential vulnerabilities, injection risks, improper authentication), performance (inefficient algorithms, unnecessary database queries, memory leaks), and maintainability (code clarity, documentation, test coverage).

These agents work by analyzing the diff, understanding the surrounding code context, and applying coding best practices and project-specific conventions. The best code review agents combine general programming knowledge with learned patterns from the specific project's codebase.

Leading Coding Agent Tools

GitHub Copilot and Copilot Workspace

Copilot provides in-editor code suggestions, while Copilot Workspace extends this to multi-file task execution. Given an issue description, Workspace creates a plan, implements the changes across multiple files, and presents the results for review. Its deep GitHub integration provides context from issues, pull requests, and repository history.

Cursor

Cursor is an AI-native code editor built around the agent paradigm. Its agent mode can make multi-file changes, run terminal commands, and iterate based on results. Cursor's strength is its tight integration between the editing experience and agent capabilities, making the transition between manual and AI-assisted coding seamless.

Claude Code

Claude Code operates as a terminal-based coding agent that can navigate codebases, edit files, run commands, and manage git operations. Its agentic approach handles complex, multi-step development tasks with the full capability of Claude's reasoning, making it particularly effective for architectural changes and complex refactoring.

The best coding agent is the one that fits your workflow. Some developers prefer IDE-integrated agents like Cursor, while others prefer terminal-based agents like Claude Code. The capabilities are converging; the interaction model is what differentiates them.

What Coding Agents Do Well

• Boilerplate and scaffolding: Generating repetitive code structures, configurations, and project setups
• Bug fixes with clear error messages: Diagnosing and fixing bugs when the error output provides clear signals
• Refactoring: Systematic code transformations that require consistency across many files
• Test writing: Generating test cases based on existing code and edge case analysis
• Documentation: Writing docstrings, README content, and inline comments

Where Coding Agents Struggle

• Novel architecture decisions: Designing new systems from scratch requires experience and judgment that agents lack
• Subtle bugs: Race conditions, memory leaks, and complex state management issues that do not produce clear error messages
• Performance optimization: Deep optimization requires understanding hardware, runtime behavior, and domain-specific constraints
• Large-scale refactoring: Changes that affect hundreds of files risk exceeding context limits and producing inconsistencies

Coding agents are improving rapidly. Each generation handles more complex tasks with greater reliability. The trajectory points toward agents that can independently handle most routine software engineering tasks while collaborating with human developers on the creative and strategic aspects of building software.