Explore the world of multi-language pipelines and learn how to leverage Apache Beam for cross-language data processing.

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  Executive Summary AI-native engineering promises faster delivery, but speed alone does not guarantee quality. As AI-generated code increases developer velocity, many teams experience new bottlenecks around code reviews, system ownership, and shared understanding. Senior engineers are increasingly burdened with review overload, while junior engineers risk shipping code they cannot fully explain. In enterprise environments, balancing speed and quality requires deliberate intent, visible context, and clear ownership. Teams that succeed treat AI as an accelerator within a disciplined engineering process—not as a replacement for decision-making. This article explores the hidden costs of unchecked velocity in AI-native engineering and outlines practical principles for scaling safely without sacrificing code quality or long-term maintainability.

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  Executive Summary The article explains how Artificial Intelligence (AI) is transforming procurement from a tactical, manual function into a strategic driver of value, innovation, and resilience. By leveraging technologies such as machine learning, natural language processing, and Generative AI, organizations can automate and optimize every stage of the procurement lifecycle—from intelligent sourcing and supplier risk management to contract analysis and the procure-to-pay (P2P) process. The rise of integrated “Agentic AI” systems enables end-to-end workflow automation, predictive risk detection, and data-driven decision-making, while maintaining a human-in-the-loop approach to ensure strategic oversight. Ultimately, AI empowers procurement teams to reduce costs, improve efficiency, and proactively manage risks, positioning the function as a key enabler of organizational competitiveness and agility.

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  MCP and AI Agents: The Next Big Shift in Engineering Workflows

  Executive Summary ML6 went to the AI Engineer summit in Paris. We are confident that agents will remain a vital part of the industry, and MCP will be at the forefront of this trend. While MCP adoption is skyrocketing, its potential remains heavily underutilized. And while many agent projects are still explorative, those that find a suitable use case can radically transform their processes through iterative engineering.