AI coding agents are the software systems that power modern AI-assisted development. Unlike simple code completion, these agents can understand what you're trying to build, navigate your codebase, execute commands, and iteratively work toward a solution.
At their core, these systems combine large language models with the ability to use tools. The language model provides reasoning and code generation capabilities. The tool interface lets the agent interact with the real world: reading files, running tests, browsing documentation, executing shell commands.
Popular AI Coding Agents
Several tools have emerged as leading AI coding agents:
Cursor is an IDE built around AI-assisted development. Its agent mode can autonomously implement features, fix bugs, and refactor code across multiple files.
Claude Code is Anthropic's command-line coding agent. It operates directly in your terminal, understanding your project context and executing complex development tasks.
GitHub Copilot and similar tools offer varying degrees of agentic capability, from simple completions to more autonomous features.
How They Work
When you ask an AI coding agent to implement a feature, it doesn't just generate code in isolation. A typical workflow looks something like this:
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The agent first tries to understand the request in context. It might read relevant files in your project to understand existing patterns, check documentation for APIs you're using, or ask clarifying questions if something is ambiguous.
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Then it formulates a plan. For complex tasks, agents will often break the work into steps: create a new file here, modify an existing file there, add tests, update configuration.
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Finally, it executes the plan, generating code and using tools to write files, run commands, and verify its work. When something doesn't work, it can read error messages and iterate.
Security Considerations
The power of AI coding agents requires responsibility. Research from Stanford has shown that these systems can introduce vulnerabilities just as easily as human developers, and potentially faster. They might generate code with SQL injection vulnerabilities, insecure authentication patterns, or improper input validation.
The challenge is that agents generate code at a pace that outstrips traditional review processes. This is where concepts like ACSM and hooks become important. They provide guardrails that operate at the speed of the agent itself.
Corridor integrates with both Cursor and Claude Code, providing real-time security analysis through hooks and MCP.