StatsClaw: Multi-Agent Claude Code Architecture for Building Reliable Statistical Software
Translating statistical methods into reliable software is a persistent bottleneck in quantitative research. StatsClaw introduces a multi-agent architecture for Claude Code that enforces information barriers between code generation and validation.
The Problem
AI code generation produces code quickly but cannot guarantee faithful implementation — a critical requirement for statistical software where correctness matters.
StatsClaw's Architecture
A planning agent produces independent specifications for three blind agents:
| Agent | Role | Cannot See |
|---|---|---|
| Builder | Implements the algorithm | Ground-truth parameters |
| Simulator | Generates test data | The algorithm |
| Tester | Validates implementation | Implementation details |
By enforcing information barriers between agents, StatsClaw ensures that bugs cannot hide through circular validation.
The Probit Case Study
Demonstrated end-to-end on a probit estimation package, showing the workflow from specification through implementation, testing, and validation.
Real-World Validation
Evaluated across three applications to the authors' own R and Python packages, proving the approach works on production statistical software.
Why It Matters
- Reproducible research — Faithful implementation of statistical methods
- AI-assisted development — LLMs handle engineering while researchers control methodology
- Quality assurance — Information barriers prevent validation circularity
- Practical adoption — Built on Claude Code, immediately usable
This represents the growing trend of multi-agent AI workflows for software development, where different AI agents play specialized roles with enforced separation of concerns.