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IDL® Agent vs. Other Agentic Solutions: What IDL Developers Need to Know

Jason Wolfe

Not all AI coding assistants have the right hooks into IDL, and choosing the wrong one could cost you time and productivity.

IDL Agent acts as a programming partner to help you write and improve IDL code. It works within Visual Studio Code (VSCode) and lets you direct the analysis and issue prompts through a chat window. It is available for customers with IDL 9.2, and it will be available at no charge with future IDL releases.

Perhaps you’re considering using a coding agent such as Claude Code or OpenAI Codex to write new IDL code or manage legacy IDL applications. This article describes why IDL Agent improves your programming flow compared to other solutions.

IDL Agent as a Coding Partner

What is the purpose of an AI agent? Whether you are creating a new algorithm or custom data-processing tool, you are the driver who oversees the entire process. An assistant like IDL Agent is a tool to help you work with IDL and has been designed to generate more consistent results and behave like a user. It does this by using specialized Model Context Protocol (MCP) tools provided by our IDL for VSCode extension, which allows an LLM to:

  • Write and execute IDL code using a standard set of instructions and syntax.
  • Reference IDL documentation for code examples and details on routines.
  • Easily inspect and validate the output from running code or IDL Notebooks.
These tools ensure that IDL Agent produces more consistent and repeatable results. It also understands your specific IDL environment. It has a deep awareness of your project’s unique structure, local library dependencies, and precise syntax rules. It provides verified, context-aware, auto-completion and error handling that is tailored to your environment.

 

creating IDL code with IDL Agent

Other coding agents tend to operate as black boxes or external assistants. They lack native access to IDL through specialized MCP tools. This means that other agents won’t consistently write code that follows IDL’s modern style, may use incorrect routine syntax or keywords, and can’t natively run your code like you might. In addition to this, our integration with VSCode provides all the tools a developer needs to take control and efficiently write code.

IDL for VSCode also includes many features for developers such as auto-completion, hover-help, native debugging, and support for IDL Notebooks. In other agentic developer environments, you won’t get the same user experience that allows you to efficiently pick up where they leave off. A revised version of the diagram above might resemble the one on the right, which operates outside of VSCode and excludes the additional developer tools:

creating IDL code with IDL Agent

A Code Example Showing the Difference

With IDL Agent, we include specialized instructions and MCP tools that help make AI-generated code correct the first time it is written. In fact, internal quality assurance tests revealed that GitHub Copilot combined with IDL Agent improved code generation accuracy by 34% when compared to GitHub Copilot alone.

Take the following prompt for example:

Using IDL, plot a sinusoidal curve of fake temperature data that shows seasonal fluctuations over one year. IDL Agent always uses function graphics and specialized instructions to create plots and diagrams, as shown by the “plot” statement in this code snippet:

The resulting plot is visually appealing due to the use of function graphics:

Entering the same prompt into a 3rd party agent produces IDL code that varies from run to run. In one example, another agent created a plot using direct graphics:

In a second run, it used function graphics to create the same plot. However, an attempt to plot a line at 0 degrees failed:

Compared to IDL Agent, these examples illustrate how you may need multiple iterations to get to the right outcome. IDL Agent's specialized instructions help you get there faster and follow modern approaches the first time.

 

Summary

IDL Agent keeps you, the developer, in full control of paired programming workflows.

By embedding AI directly into a rich, native developer environment such a VSCode, IDL Agent and the human developer leverage the exact same underlying toolset. This creates a seamless pair-programming dynamic where you can intervene, guide, or take over at any moment, yielding higher accuracy, faster debugging, and complete control over the process.

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