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TOPIC · ENTITY #487

enterprise AI

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EVENT TIMELINE

3

RESEARCH · 1 SOURCE · arXiv cs.AI

arXiv preprint proposes AGIL, a five‑layer ML architecture for real‑time AI policy enforcement and attestation

This arXiv cs.AI preprint identifies an "attestation deficit" in enterprise AI governance and proposes AGIL (Adaptive Governance Intelligence Layer), a theoretical five-layer architecture that uses ML for real-time policy enforcement, inline permit/deny/modify decisions at sub-100 ms latency, and continuous tamper-evident attestation; the authors draw on reported industry data (e.g., 78% AI adoption, 362 incidents cited) but note that empirical validation through controlled deployment is future work.

6.0

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

Integrating DMN Decision Models, LLMs and NeMo to Produce Auditable Agentic Architectures

Alex Porcelli outlines a response to a key enterprise AI problem—non-deterministic outputs and lack of accountability—by describing how combining DMN decision models with LLMs, agent skills and NeMo guardrails can yield auditable, deterministic agentic architectures that let business leaders own decision logic while engineers retain architectural governance.

6.0

COMPANIES · 1 SOURCE · MIT Technology Review AI

Facilitating AI integration with simplicity at scale — MIT Technology Review looks at Jabil

An MIT Technology Review AI article highlights how scaling companies can accumulate disconnected systems, site-specific tools, spreadsheets and manual workarounds that create data silos and impede early problem detection and confident decision-making, citing global manufacturer Jabil as an example. The piece discusses the need to simplify technology stacks to enable broader operational AI integration at scale.

5.0