review-synthesis

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.

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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.

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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 Jacob