While general-purpose LLMs excel at text, they often merely simulate the coding process rather than executing it. There are some critical boundaries of generative AI platforms—from sandboxing to verification—specialized agentic assistants are the true key to a functional AI-driven development lifecycle.
While a general purpose Generative AI Platform is very useful, such LLM platforms like Google Gemini, Claude, ChatGPT, etc. are not effective for software development. They are not a one-stop solution to generating/compiling/running code automatically.
They are not an operating system sandbox (Linux, Windows, etc.) on which a user may install software and run shell commands. They can provide a user with the Linux/Windows commands for specific use; however, they can’t run the commands themselves.
They are not a compiler/interpreter for programming languages on which a user may select a language, and compile the code. They are not a runtime environment for programming languages either. If you ask it to compile an application, you are likely to get an output similar to the following:
To "compile" this code, I can explain the process and provide the output as if it were running in a real terminal environment. Since I am an AI, I don't "compile" into a binary file for you to download, but I can simulate the execution!
LLMs such as the CodeLlama, which are designed specifically to generate code, don’t compile or run the code they generate.
While a Gen AI LLM based tool can suggest likely issues with code, it can’t debug the code. I asked Google Gemini to “debug the application”:
public class HelloWorld {
public static void main(String args) {
Syste.out.println("Hello, World!")
}
}
The output indicated “three specific syntax errors”, and listed the “debugged code”:
public class HelloWorld {
public static void main(String[] args) {
System.out.println("Hello, World!");
}
}
However, the Gen AI platform doesn’t actually run the erroneous code or the debugged code. The output, “When you try to compile the original version, the Java compiler generates errors that look like this:” may be ambiguous to some; however, it does indicate that these are only “approximate” compiler messages.
They are not a code lint, a static code analyzer that checks for errors, bugs, and style issues. It simulates all the functions of a code lint, but does not actually run a lint software for it.
General purpose Generative AI platforms are not designed to ascertain whether all the components of a software environment are compatible, i.e., supported when used together. If a user is requesting code for a specific software environment, or finding issues with code when run in a combination of software, a Generative AI tool may not detect the incompatibility among the different software used and may generate code and recommend fixes regardless.
The generic generative AI platforms can’t verify the code they generate. A user is expected to compile, run, and verify the code.
This is because LLMs are fundamentally not designed for code generation. LLMs are designed to discover patterns in text for understanding and processing text, and for text generation. Multimodal LLMs (MLLMs), which can process images, audio, and video, do so by converting the different types of input (text, image, audio, video) into tokens and projecting them into a common, high-dimensional vector space.
However, AI code-assistants like Claude Code, Copilot, OpenAI (Operator), and Amazon Q are good solutions that do support these key activities of compiling/debugging code, making them the best option for generating code. Further, they have agentic capabilities, implying that they act like agents to orchestrate multiple coding activities such as generating, compiling, debugging, and running code.
While the standard Claude doesn’t compile or run code, the Claude Code coding assistant introduced in 2025 can be integrated with code development environments, including the terminal, CLI tools such as Git and GitHub, and IDEs such as VS Code and IntelliJ, to fix bugs, test code, refactor code, and implement features. Furthermore, Claude Code asks for permissions before running commands or making changes to codebase files. Claude Code can also integrate with web, application, and inference servers using the Model Context Protocol (MCP) for AI tool integration.
As noted in a recent research article, Where Do LLMs Still Struggle?, code generation with LLMs has some limitations.
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