Improve AI Code Generation Using NVIDIA NeMo Agent Toolkit

Cross-section page — Agent Development angle. See primary page for the full summary.

Agent Development Angle

This article is a hands-on tutorial for building a multi-agent coding system using NVIDIA-NeMo-Agent-Toolkit and LangGraph, illustrating practical agent design patterns that apply broadly to any agentic workflow.

Agent Design Patterns

Flow engineering is the central design philosophy: define states and transitions explicitly (like a state machine), but allow an agent or tool to operate freely within each state. This gives predictability at the system level without sacrificing agent autonomy within subtasks.

The test-driven loop is a reusable pattern for any task with a verifiable success condition:

  1. Generate a candidate solution
  2. Execute a validator (unit tests, linter, runtime check)
  3. If validation fails, invoke a reasoning model to diagnose the error
  4. Revise and retry up to a budget limit

Supervisor + specialist pattern: the coding agent is itself registered as a callable tool, allowing a ReACT-style supervisor to orchestrate it alongside other agents (research, error localisation, test generation) asynchronously. This composable architecture scales to complex software engineering tasks.

LangGraph Integration

The coding agent is implemented as a LangGraph graph and registered as a function in Agent Toolkit. The YAML config declares the graph entry point, the models for each node, and the tool definitions. Swapping the reasoning model from DeepSeek-R1 to another model requires only a config change — no code rewrite.

Evaluation-Driven Development

aiq eval enables testing the full workflow against expected outputs. Developers define correct solutions, run evaluations after each config change, and compare metrics across variants. This tight eval loop prevents regression and enables confident iteration.

Connections