Enterprise Superintelligence

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.

Executive Summary Choosing an agentic AI framework is an architectural decision, not a tooling choice. While many frameworks offer similar building blocks, they differ significantly in abstraction level, ecosystem coupling, observability, governance, and production readiness.

Executive Summary Agentic workflows are AI-driven systems designed to reason, plan, and act autonomously across complex tasks and environments. Unlike prompt-based interactions or deterministic automation, they coordinate multiple AI agents, tools, and feedback loops to achieve high-level goals under uncertainty. This article introduces the core components of an agentic workflow, explains how autonomous AI agents operate within them, and clarifies when agentic approaches are appropriate for production AI systems.

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.