Agent for Advanced Secure Code Synthesis
Nvidia's introduction of Nemotron 3, with its hybrid Mixture-of-Experts and Mamba-Transformer architecture, presents a new frontier for specialized code generation. This challenge focuses on building an advanced agent capable of leveraging the Nemotron 3 model to synthesize highly optimized and secure code for a specific, complex domain (e.g., embedded systems, financial algorithms, or scientific computing). Your solution will employ DSPy for programmatically optimizing the prompts and modules that guide Nemotron 3's code generation, ensuring high quality, correctness, and adherence to security best practices. The agent will utilize extended thinking with adaptive reasoning budgets to iteratively refine, test, and debug generated code. Crucially, MCP (Model Context Protocol) will enable seamless integration with external tools such as static code analyzers, unit testing frameworks, and version control systems, allowing the agent to perform comprehensive validation and integrate its output into a development pipeline.
What you are building
The core problem, expected build, and operating context for this challenge.
Nvidia's introduction of Nemotron 3, with its hybrid Mixture-of-Experts and Mamba-Transformer architecture, presents a new frontier for specialized code generation. This challenge focuses on building an advanced agent capable of leveraging the Nemotron 3 model to synthesize highly optimized and secure code for a specific, complex domain (e.g., embedded systems, financial algorithms, or scientific computing). Your solution will employ DSPy for programmatically optimizing the prompts and modules that guide Nemotron 3's code generation, ensuring high quality, correctness, and adherence to security best practices. The agent will utilize extended thinking with adaptive reasoning budgets to iteratively refine, test, and debug generated code. Crucially, MCP (Model Context Protocol) will enable seamless integration with external tools such as static code analyzers, unit testing frameworks, and version control systems, allowing the agent to perform comprehensive validation and integrate its output into a development pipeline.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master DSPy for programmatically constructing and optimizing prompts, signatures, and modules to control Nemotron 3's code generation process for specific languages and paradigms.
Implement MCP-enabled tool integration with a simulated static code analyzer (e.g., SonarQube, Bandit), a unit testing framework (e.g., Pytest, JUnit), and a version control system (e.g., Git).
Deploy Nemotron 3 (simulated access) for generating complex code structures, functions, and modules based on detailed specifications, focusing on security and performance considerations.
Build an extended thinking agent workflow where Nemotron 3 generates initial code, followed by DSPy-orchestrated calls to MCP-enabled analysis tools for feedback, and iterative refinement based on their output.
Integrate OpenAI o3 as a planning and refinement model, guiding Nemotron 3's generation, especially for complex architectural decisions or debugging steps.
Apply adaptive reasoning budgets within the DSPy pipeline, allocating more 'thinking' cycles and model calls (e.g., to Nemotron 3 for re-generation) when static analysis tools report critical vulnerabilities or test failures.
How this agent runs
The generated code will be evaluated for correctness, adherence to specifications, security vulnerabilities (via static analysis), and passing unit tests. The efficiency of the DSPy pipeline and MCP tool integration w...
Challenge input
{'task_description': 'str', 'security_constraints': 'list_of_strings', 'language': 'str'}
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{'generated_code': 'str', 'static_analysis_report': 'dict', 'unit_test_results': 'dict', 'agent_reasoning_log': 'list'}
- Generated code for security-sensitive tasks has zero critical vulnerabilities reported by static analysis.
- All provided unit tests for generated or refined code pass successfully.
- Code Quality Score (From Linter) target: 8.5
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
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[ok] Wrote eval/examples.json
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