How to Build an AI Code Generation Agent in NVIDIA AgentIQ
Why Build Coding Agents with AgentIQ
Large Language Models (LLMs) excel at coding tasks but are limited to a chat interface, lacking autonomy and integration with the real world. In contrast, AI agents, powered by these LLMs, are designed to accomplish real-world goals. They often interact with their environment using tools, memory, and planning to execute tasks such as file editing, code execution, or information search.
AI Agent Design Considerations
AI agents are one example of scaling inference-time computation for improving AI performance. To build an agent or multi-agent system, you must balance flexibility against structure.
Ease of AI Agent Development with AgentIQ
Evaluation, deployment, and optimization are a few common challenges developers can resolve with AgentIQ. The following table summarizes some of the features and benefits of AgentIQ.
Tutorial Prerequisites
You need the following setup:
- Clone the NVIDIA/AgentIQ GitHub repo
- Install the AgentIQ library
- Create a new project template using the AIQ scaffold command
How to Build an AI Code Generation Agent in NVIDIA AgentIQ
In this post, we guide you through integrating AI agents and reasoning models to create an AI code-generation agent in AgentIQ. We build the core agent using LangGraph, integrate a sandbox code execution tool for safety and control, and enhance error correction with DeepSeek-r1. Lastly, we show how the agent can be integrated into a larger system using a supervisor agent.
Set up the project scaffold
First, clone the NVIDIA/AgentIQ GitHub repo. Follow the instructions in the README to install the AgentIQ library. Now create a new project template using the AIQ scaffold command.
Code Generation Agent Example
The input to the code generation agent is a problem statement, code to fix, and unit tests. The agent follows a simple process:
- Use a code LLM for code generation to create a git patch that resolves the issue.
- Run the updated code against the unit tests in a safe code execution sandbox.
- If the test fails, a reasoning model will suggest changes based on the output.
- Steps 1-3 repeat until either the generated code passes the desired unit tests, or the maximum number of iterations is exceeded.
Update the Configuration File
The configuration file in AgentIQ defines the entire workflow. By updating the configuration file, such as adding tools (functions), swapping LLMs, or changing other components, agentic workflows can be rapidly developed and iterated on.
Conclusion
Code generation problems are excellent candidates for test-time compute scaling because it’s possible to identify when a solution is correct. For example, a test-driven development agent can iterate on proposed solutions, with the number of iterations limited only by a compute budget. Reasoning LLMs such as DeepSeek’s R1 model provide reflections that can accurately guide a code generation model through a debugging process. Agentic tool use, memory, and planning can be integrated to improve the system.
Frequently Asked Questions
Q: What is AgentIQ?
A: AgentIQ is an open-source library for connecting and optimizing teams of AI agents.
Q: What are the benefits of using AgentIQ?
A: AgentIQ provides features such as inclusive agent framework, common specification, evaluation, deployment, and optimization.
Q: How do I get started with AgentIQ?
A: You can start by cloning the NVIDIA/AgentIQ GitHub repo and following the instructions in the README to install the AgentIQ library.
Q: What are the system requirements for running AgentIQ?
A: The system requirements for running AgentIQ are not specified in this article. For more information, please refer to the NVIDIA AgentIQ documentation or GitHub repo.
