Microsoft announced the general availability of Microsoft Discovery, its Azure-based platform for deploying autonomous AI agent teams in scientific R&D. The platform powered the development of...

易点天下在Agentic AI领域的工程化实践中，通过上下文工程和多云基础设施推动AI Agent的规模化应用。公司构建了多云架构，实现资源统一调度，并以“Context Engineering”为核心，形成六层上下文体系，提升推理效率和工具调用准确率，同时建立五层防御机制以降低风险。

Insurers have long understood the need to transform their core technologies. So, why haven’t they? Agentic AI may finally make the difference.

Scaling agentic AI requires turning unstructured data into governed, reusable assets that systems can interpret and trust. Data leaders can start by building shared foundations and enforcing standards.

For marketers, AI-enabled workflows will fuel new levels of growth, speed, and efficiency. What is your activation plan?

With half of US organizations in our survey now implementing gen AI, the focus is turning to integration, ROI, and agentic AI to realize value across healthcare domains.

Agentic AI scales on strong data. To capture value, tech leaders can agentify high-impact workflows, modernize data architectures, enforce data quality, and evolve operating models.

Agentic AI can automate routine work and improve merchandising decisions at scale. Two McKinsey experts discuss what it takes to translate potential into performance.

Agentic AI is all about doing things—just like operations. McKinsey partner Michael Chang explains its potential, why waiting to implement could be risky, and the keys to realizing impact.

Paul Duvall recently discussed his library of engineering patterns for AI assisted development and practices that ground high quality delivery. Related discussions from Paul Stack and Gergely...