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Sprints were originally designed to manage slow, uncertain execution in software development. As AI agents compress coding and iteration cycles from weeks to hours or days, execution is no longer the primary bottleneck for many teams. Keeping two-week sprints unchanged often shifts time into waiting, over-polishing, or coordination overhead. Rather than abandoning sprints, teams need to redefine their purpose. In an agentic environment, sprints should function as a cadence for decision-making, alignment, and risk management, while execution flows continuously. The real question is not whether sprints still matter, but what problem they are meant to solve now.

The idea of running many AI coding agents at the same time is powerful, but often misunderstood. When people claim they have ten or fifteen agents working in parallel, they rarely mean ten agents editing the same codebase simultaneously. What they usually mean is that they have many independent AI contexts working on different problems, and they orchestrate the results.

I built a fun web app called Gesture Fireworks using the Cursor AI coding agent and published it on my personal site. Friends loved it and asked how they could create something similar. This post breaks down the exact process I followed, step by step. The key takeaway is simple: effective collaboration with an AI agent is less about writing perfect prompts and more about having a clear workflow. I started with inspiration, let the AI guide early design decisions, gave short and specific feedback, asked for research when needed, and iterated quickly. With this approach, I was able to go from idea to deployed app with minimal friction. The same method can be used for almost any small creative project.

The Wall Street Journal recently reported on the rapid rise of Claude Code and how quickly it has spread beyond the usual circle of AI enthusiasts. That article should cause many CEOs and technology leaders to pause and reconsider the AI strategies they have already committed to. For the last couple of years, most enterprises have followed a very familiar playbook. They selected an approved corporate platform, standardized on a vendor stack such as Gemini Enterprise or Microsoft Copilot, launched formal transformation programs, and brought in outside partners to implement carefully defined use cases. This approach made sense because it mirrored how organizations adopted cloud computing, analytics, and ERP systems. It was orderly, governable, and predictable. ...

Modern AI agents can plan, reason, and execute complex software tasks. But capability alone is not enough. Agents need context, structure, and feedback loops to be effective teammates. This framework shows how to give them what they need.

As AI agents become capable of planning and taking action, users increasingly stop interacting with applications directly. What does this mean for software companies?

TL;DR: I got tired of yelling “Hey Google” at my smart speaker. So I paired with Claude Code to build command-line scripts that control Spotify on my Google Home. Network discovery → API setup → working scripts in one session. Now I control my house from the terminal like a proper developer. The Problem That Started It All Picture this: You’re deep in flow, coding with headphones on. You want to change the music on your Google Home speaker in the other room. Your options? ...