npm i -g aiwg        # install globally
aiwg use sdlc        # deploy SDLC framework

What AIWG Actually Is

AIWG is a cognitive architecture that provides AI coding assistants with structured memory, ensemble validation, and closed-loop self-correction. Unlike simple prompt libraries or ad-hoc workflows, AIWG implements research-backed patterns for multi-agent coordination, reproducible execution, and FAIR-aligned artifact management. The system addresses fundamental challenges in AI-augmented development: recovery from failures, maintaining context across sessions, preventing hallucinated citations, and ensuring workflow reproducibility. These capabilities position AIWG closer to cognitive architectures like SOAR and ACT-R, adapted for large language model orchestration, than to conventional AI development tools.

Why This Matters

For Practitioners

Turn unpredictable AI assistance into reliable, auditable workflows. Research shows 47% of AI workflows produce inconsistent results without reproducibility constraints. AIWG implements closed-loop self-correction, human-in-the-loop validation (reducing costs by 84%), and retrieval-first citation architecture (eliminating the 56% hallucination rate of generation-only approaches). 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.

For Executives

Risk reduction through governance-ready AI workflows. AIWG provides audit trails (W3C PROV provenance chains), quality gates (GRADE-style evidence assessment), and deterministic execution modes. The system implements stage-gate processes familiar from Cooper's methodology, ensuring predictable phase transitions and milestone tracking. Standards adopted by 100+ organizations (WHO, Cochrane, NICE) back the quality assessment approach. Human validation checkpoints ensure AI outputs meet enterprise quality standards before production deployment.

Research Foundations

AIWG's architecture is informed by established research across cognitive science, software engineering, and AI systems. The cognitive load optimization follows Miller's "7±2" limits and Sweller's worked examples approach. Multi-agent ensemble validation implements mixture-of-experts patterns from Jacobs et al. The closed-loop self-correction design addresses the finding that recovery capability—not initial correctness—dominates agentic task success. Research management implements FAIR Principles with 17,000+ citations and institutional backing from G20, EU Horizon 2020, and NIH. The retrieval-first citation architecture eliminates hallucination by grounding all references in verified sources rather than generative recall.

Full research background, citations, and methodology available in docs/research/.

Core Capabilities

1. Structured Semantic Memory

Persistent artifact repository (.aiwg/) maintaining project knowledge across sessions. Implements retrieval-augmented generation patterns to prevent context loss when AI assistants restart or hit token limits.

2. Multi-Agent Ensemble Validation

Specialized agents (Test Engineer, Security Auditor, API Designer) provide domain expertise with coordinated review and synthesis. Mixture-of-experts arch

...