Turn business outcomes into an architecture that can be evaluated and operated. This prompt connects requirements, constraints, alternatives, trust boundaries, failure behavior, costs, migration, and validation evidence.
Use Global Workspace Theory as a lens for coordinated agents. Design selection, shared state, evidence broadcasting, and authority boundaries without confusing a large context window with a complete coordination architecture.
Test the complete agent coordination loop under failure. Exercise duplicate delivery, interrupted execution, delayed evidence, and changed permissions while measuring task completion separately from control failures.
Keep shared evidence, human approval, and execution authority distinct. Preserve provenance, bind permission to exact operations, and revalidate the boundary when an agent is ready to act.
Design a bounded shared workspace for AI agents with versioned state, visible disagreement, and controlled updates. Separate current task state from recovery history and reusable memory.
TL;DR Boards should not approve “AI at scale” as a broad technology initiative. They should approve a bounded portfolio of AI use cases with measurable value, named owners, governed data, production-ready architecture, constrained authority, tested controls, workforce readiness, and a credible exit path. This scorecard gives CEOs and boards 12 questions to ask before enterprise … Explore: The Board-Level AI Readiness Scorecard: 12 Questions CEOs…
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