Coding agents make implementation cheaper. The scarce skills move to framing the problem, specifying intent, evaluating results and owning the outcome. A map of six shifts and the evidence behind each.
When coding agents compress implementation from weeks to hours, the scarce skill is knowing what is worth building. What Atlassian means by a product engineer, and a practical routine for becoming one.
A vague requirement used to cost a day of rework. With agents it can cost a plausible-looking pull request in minutes. Why problem framing, specification and evaluation are becoming one skill, and how to practise it.
Teams can watch their AI systems far more easily than they can judge them. What the 2026 agent engineering data says, and a minimal error-analysis loop for turning "what does good look like?" into an engineering artifact.
A working prototype is now a commodity; reliability is not. What Stripe's real-integration benchmark, Vercel's deployment data and OpenAI's Agents API say about the harness around the model, and a checklist for crossing the gap.
Giving every developer an AI tool is not the same as becoming AI-native. What Microsoft, McKinsey and OpenAI describe about redesigning workflows so humans and agents work together, and how a small team can start.
Agents can scale almost without limit; human attention cannot. Addy Osmani on where judgment should sit in a software factory, what a 2026 field experiment says about expertise, and how to keep your own judgment sharp.
Steering helps you define rules, style, and workflows so agents follow your team’s conventions instead of inventing their own.
Kiro turns prompts into EARS-style requirements, designs, and task plans—helping you move from “ideas” to shippable, verifiable code.
