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The following article originally appeared on Addy Osmani’s blog site and is being republished here with the author’s permission. Coding agents are extraordinarily good now, and getting better fast. The interesting consequence is that the hard part of engineering moved from writing code to deciding whether to trust it, which makes review the most leveraged skill in software right now.

This week host and Turing Post founder Ksenia Se threaded the latest news into a single argument: AI is moving out of conversation and into the operational loops where real work happens. From SpaceX’s $60 billion acquisition in the developer tools market to the G7’s debate about frontier model access to image generation company Midjourney’s pivot to medical hardware, the stories all pointed in the same direction.

I got a really interesting question last week from Mike Loukides, my editor at Radar, after he read the third part of this trilogy on context management. “Another issue I’ve read about,” Mike asked, “is the tendency for a model to ignore the middle of the context. I’ve seen that particularly for the models with very large context windows. Is there anything to be said about that?” Excellent question, Mike, and yes, there is.

In 2025, if you weren’t building with MCP, you weren’t serious about agents. The Model Context Protocol dominated the agent conversation for the better part of the year. Conference talks, roadmaps, hiring plans, all of it revolved around MCP. Then late 2025 into 2026, AI Skills arrived and the backlash was immediate. Engineers declared MCP dead in favor of Skills, then dead in favor of CLI. Perplexity’s CTO said publicly that the company was deprioritizing it.

Over the past year I’ve reviewed enterprise agent architectures at roughly two dozen organizations, including banks, retailers, healthcare systems, and a couple of regulators. The architecture diagrams have been reliably impressive. There are boxes for the MCP gateway, the tool registry, the vector store, the orchestrator, the policy engine, and the observability stack. There are arrows showing how agents discover each other, share context, and call tools across the mesh.

The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. Loop engineering is replacing yourself as the person who prompts the agent. You design the system that does it instead. A loop here can be thought of as a recursive goal where you define a purpose and the AI iterates until complete. I believe this may be the future of how we work with coding agents.

When Kubernetes first came onto the scene, it was a major turning point, a revision of the infrastructure and operations space that transformed the way developers and ops personnel build, deploy, and maintain applications in the cloud. It has since become the clear standard for how modern applications are built and operated. As the CNCF noted in its latest Annual Cloud Native Survey report, “Among container users, 82% are using Kubernetes in production in 2025, up from 66% in 2023.

You know the meeting. The board wants an AI agent strategy by end of quarter. Someone on the leadership team has read a McKinsey report. You’ve been voluntold to build the platform. The slide deck says “AI-native.” The acceptance criteria are vague. Somebody mentions LangGraph, and somebody else says, “We’ll just wrap it ourselves.” You ask what “done” looks like. Nobody in the room can answer.

Many AI agent systems become economically unsustainable long before they become technically impressive. Teams usually focus on model choice, prompt design, tool calling, and orchestration. Those things matter, but they are only part of the system setup. The deeper issue is that coding agents, such as Claude Code, Codex, and Jules, make agent workflows easier to generate. But when implementation is abstracted away, the underlying mechanics become harder to see.

The following article originally appeared on Sena Evren’s Legal Layer newsletter and is being reposted here with the author’s permission. TL; DR Agentic coding tools like Claude Code, Cursor, and Codex generate code that may be uncopyrightable, owned by your employer, or contaminated by open source licenses you cannot see. Some of this is settled law, some is actively contested, and this piece is clear about which is which.