Why This Matters

For Practitioners

Turn unpredictable AI assistance into reliable, auditable workflows. Research shows many AI workflows produce inconsistent results without reproducibility constraints. AIWG implements closed-loop self-correction, human-in-the-loop validation, and retrieval-first citation architecture that grounds all references in verified sources rather than generative recall. The .aiwg/ artifact directory provides persistent memory across sessions, ensuring context isn't lost when your AI assistant restarts.

For Researchers

Standards-aligned implementation of multi-agent systems and reproducibility frameworks. AIWG operationalizes FAIR Principles (endorsed by G20, EU, NIH), implements OAIS-inspired archival lifecycles (ISO 14721), and uses W3C PROV for provenance tracking. The framework provides a testbed for studying human-AI collaboration patterns, ensemble validation effectiveness, and cognitive load optimization in AI-augmented workflows. All artifacts are structured for analysis and citation export....