Current AI agent orchestration frameworks — LangChain, CrewAI, AutoGen, and their successors — solve the problem of execution: how to chain tools, manage context windows, and coordinate multiple agents toward a goal. What they do not solve is the problem of governance: constraining what an agent should know, evaluating how fresh its knowledge is, enforcing which ethical boundaries it must respect, and determining when collective deliberation should override individual agent judgment. This paper introduces AGORA-OS, a seven-layer semantic operating system architecture designed to fill this governance gap. The seven conceptual layers — Constitution (C1), Orchestrator (C2), Semantic Slicing (C3), Council (C4), Health and Governance (C5), Delivery (C6), and Observability (C7) — provide a complete framework for deploying AI agents in regulated, high-stakes domains. The architecture is grounded in the Artisanal Intelligence Program and validated against real deployment scenarios in Brazilian legal AI systems.
Renato Aparecido Gomes (2026) studied this question.