"You will never learn anything new [if it's not] significantly different from what you already know"
Adapted from Rich Hickey - Simple made easy
How to Succeed with Agentic Engineering - Part 3
Learn how to make a codebase easier for AI coding agents to navigate and validate using repo-local documentation, behavior specs, executable architectural guardrails, fitness functions, and narrow verification loops.
How to Succeed with Agentic Engineering - Part 2
Learn how to design a maintainable, AI-agent-friendly codebase. The key principles are locality, small blast radius, boundary integrity, navigability, cohesive modules, ownership-aligned boundaries, and restrained complexity, so coding agents can build a correct local model and change software safely.
How to Succeed with Agentic Engineering - Part 1
AI is already strong at local implementation, structured trade-off analysis, and targeted codebase exploration, but it becomes unreliable when hidden context and broader system consequences matter. This introduction explains that tension and frames the three skills developers need to use coding agents effectively.