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This paper introduces AI-GRACE, a framework for operationalizing agentic AI by connecting organizational objectives and obligations to deployment capabilities and architecture, ensuring effective governance and risk management.
Phorecaster365 presents a human-supervised reference architecture for pharmaceutical sales forecasting that integrates data ingestion, modeling, and governance with a validation framework, though it does not establish real-world accuracy.
This paper proposes the Agent Operating System (AOS), a vendor-neutral reference operating architecture for distributed agentic systems, covering governance, runtime coordination, and reliability. It aims to provide a stable framework for composing heterogeneous components into governable and observable agentic systems.
Sharing 5 key takeaways from Google Cloud's multi-tenant agent AI system reference architecture, which is inspiring for indie developers and small teams to productionize Agents.
This paper presents a five-plane reference architecture for runtime governance of production AI agents, addressing security risks from delegated actions. It defines primitives, invariants, and an evaluation framework to ensure safety and utility.