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OpenAI released an updated Model Spec that outlines five core values aimed at guiding more capable AI toward sound judgment, preserving human agency, and staying faithful to human intent. The update frames these values as principles to inform development and behavior of increasingly capable models.
OpenAI and ServiceNow expanded their partnership to bring OpenAI models and agents into the ServiceNow platform and to expose ServiceNow workflows in ChatGPT and Codex.
At QCon AI, Manju Rajashekhar (founder & CEO of Mad Labs) presented “Actors for Agents,” arguing that the actor model maps naturally to the runtime needs of production AI agents. He described patterns — state ownership, message passing, write-through state, and supervision boundaries — to manage failures, continuations, and mutable execution graphs so agentic demos can become resilient production systems.
The piece argues enterprise AI is moving from isolated experiments to infrastructure, so organizations should measure 'effective cost per useful outcome'—the combined platform, model, operations, and capacity cost to produce usable business results—and optimize utilization via scheduling, model selection, GPU partitioning, and cache management. It highlights HPE Private Cloud AI as a production-ready integrated stack that, according to independent testing commissioned by HPE, can deliver lower effective cost at high utilization, and recommends choosing between on‑demand APIs, hyperscalers, or integrated private platforms based on workload predictability and scale.
In a recent GitHub Podcast episode, hosts argue developers remain responsible for generated code and should apply different review rigor depending on risk. They position the Model Context Protocol (MCP) as a standard for tool/data access, describe 'skills' as human-readable packaged expertise, and say RAG (retrieval-augmented generation) remains useful alongside agents and skills rather than being obsolete.
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