Research
Practitioner-led research on enterprise AI systems, governance, autonomy, and the operating models needed to make AI work responsibly at scale.
Coordination Overhead and Governance Readiness in Distributed LLM Multi-Agent Systems: An Empirical Architecture Evaluation
Reports a controlled benchmark of six distributed LLM multi-agent architectures, showing strong architecture effects on coordination overhead, latency, and token use, with limited practical quality differences in short-horizon tasks.
AI-Driven Autonomous Enterprises and the Future of Work
Reviews evidence, design patterns, governance standards, and labor-market research for AI-driven autonomous enterprises, arguing for frontier-conditioned autonomy instead of a simplistic copilot-to-autonomy roadmap.
AI-Driven Corporate Climate Risk Decision Systems for Global Enterprises
Introduces the Climate AI Decision System (CADS) architecture for evidence-grounded climate-risk management across disclosure, Scope 3 traceability, governance, auditability, and enterprise decision workflows.