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AI Native Development
How software engineering changes when AI agents write much of the code. Thinking for yourself, deciding how much rigour a system needs, harness and context engineering, and reviewing AI-generated work.
- Critical thinking
- Code quality
- Harness engineering
- Context engineering
- Code review
- 1 Are You Becoming a ‘Meat Proxy’ in the AI Era? Getting answers from AI and truly understanding them are two different things. What value do we add when we simply pass questions and answers along?
- 2 Does Every Piece of Software Need the Same Code Quality in the AI Era? The skill is deciding how much engineering effort each piece of software needs, based on its lifespan, dependencies and consequences of failure.
- 3 AI Harness Engineering — Software Engineering Beyond the Prompt A capable model alone does not guarantee reliable software. Its tools, context, execution environment and controls matter too.
- 4 AI Coding Agents and the Doom Loop — The Real Cost of Poor Context When an agent keeps changing code without solving the problem, the missing ingredient may be relevant, current and trustworthy context.
- 5 Code Review and Review Tax in the Agentic AI Era — Does Every PR Need the Same Review? AI can generate pull requests quickly. Getting those changes safely into production requires review effort that matches their risk.
- 6 AI Software Factory — How Is Software Development Changing? Connect AI agents, automated tools and human oversight into a repeatable workflow across the software development lifecycle, and improve the system that builds your software.