Enterprise Superintelligence

Executive Summary Managed Agent Solutions (MAS) are cloud-managed platforms for building, deploying, and operating AI agents. They remove the need to manually set up infrastructure for orchestration, memory, tooling, tracing, guardrails, and evaluation, allowing teams to reach production faster. MAS have matured from early, UI-focused experiments into platforms that support production workloads across many GenAI use cases. They are not a universal solution. Custom agent solutions are still required when teams need advanced observability, custom evaluation, strict cost control, portability, or complex orchestration.

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.

Executive Summary At the AI Engineer Code Summit in New York City, Anthropic shared key insights into the Claude Agents SDK that reshape how effective AI agents are built in practice. By exposing the same agent harness that powers Claude Code , the SDK highlights a shift away from prompt-centric approaches toward more structured, reliable agent architectures. These learnings reflect a growing challenge many teams are encountering in practice: increasing model capability and code generation speed without losing control, auditability, or reliability. This post distills the core technical takeaways and explains why the infrastructure around the model—the agent harness—is just as critical as the model itself. The full workshop recording from the summit is available on YouTube . In this blog post, we dive into our main learnings.

AI is redefining software development — faster than many expected. In the article by De Morgen, our Senior Engineer Niels Rogge shares his perspective on the rapid rise of AI-powered software development and the explosion of so-called “vibe coding” — building applications largely generated by AI. Thanks to advances in AI coding agents, tasks that once took weeks can now be completed in days. But speed isn’t everything. As Niels puts it:

Ghent-based ML6 to make Belgium’s Federal Public Service of Justice more transparent and efficient.

AI in protein folding: a virtual playground Protein folding — the way amino acid chains fold into 3D structures — has long been one of the hardest problems in biosciences. With breakthroughs like DeepMind’s AlphaFold, recently awarded the Nobel Prize for Chemistry, predicting these structures is now faster and more accurate than ever.