Building the foundations for agentic AI at scale - McKinsey & Company
Building the foundations for agentic AI at scale McKinsey & Company
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Agentic AI deployment best practices: 3 core areas
The demos look slick. The pressure to deploy is real. But for most enterprises, agentic AI stalls long before it scales. Pilots that function in controlled environments collapse under production pressure, where reliability, security, and operational complexity raise the stakes. At the same time, governance gaps create compliance and data exposure risks before teams realize... The post Agentic AI deployment best practices: 3 core areas appeared first on DataRobot .
Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use
The landscape of open-source artificial intelligence has shifted from purely generative models toward systems capable of complex, multi-step reasoning. While proprietary reasoning models have dominated the conversation, Arcee AI has released Trinity Large Thinking. This release is an open-weight reasoning model distributed under the Apache 2.0 license, positioning it as a transparent alternative for developers [ ] The post Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use appeared first on MarkTechPost .
The agentic AI cost problem no one talks about: slow iteration cycles
Imagine a factory floor where every machine is running at full capacity. The lights are on, the equipment is humming, the engineers are busy. Nothing is shipping. The bottleneck isn’t production capacity. It s the quality control loop that takes three weeks every cycle, holds everything up, and costs the same whether the line is moving... The post The agentic AI cost problem no one talks about: slow iteration cycles appeared first on DataRobot .
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How to build an agentic AI governance framework that scales
Agentic AI is already reshaping how enterprises operate. But most governance frameworks aren t built for it. AI agents are most successful when they work within human-defined guardrails: governance frameworks designed for autonomous systems. Good governance doesn t limit what agents can do. It defines where they can operate freely, and makes it safe to give them... The post How to build an agentic AI governance framework that scales appeared first on DataRobot .
AI models fail at robot control without human-designed building blocks but agentic scaffolding closes the gap
A new framework from Nvidia, UC Berkeley, and Stanford systematically tests how well AI models can control robots through code. The findings: without human-designed abstractions, even top models fail, but methods like targeted test-time compute scaling closes the gap. The article AI models fail at robot control without human-designed building blocks but agentic scaffolding closes the gap appeared first on The Decoder .
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