The framework addresses current AI agent limitations by treating inter-agent and agent-human handoffs as explicit contracts, not implicit trust, to improve reliability and accountability. It introduces a 9-stage engine covering dynamic assessment, contract-first decomposition, market-based assignment, multi-objective optimization, adaptive coordination, monitoring, reputation, permission handling, and verifiable completion. This approach suggests that coordination, not raw model intelligence, is the primary bottleneck for agentic systems, potentially allowing less powerful models to outperform larger ones.
Agentic AI deployment is bottlenecked by coordination and accountability, not model intelligence, shifting focus to robust delegation frameworks over raw compute scale.