AI DevelopmentThe Pragmatic Engineerby Gergely Orosz

Context engineering with Dex Horthy

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“Shipping unread code spells disaster within months. We had the model write code with zero human review in July 2025. Four months later, production broke—a misrouted primary key buried in spaghetti code took days to find and three weeks to re-onboard the team to.”

Key takeaways

  • Context engineering is becoming critical competency for LLM-era engineers—frameworks like LangChain/CrewAI are being abandoned by practitioners in favor of custom pipelines built on first principles
  • Human code review is non-negotiable: unreviewed AI-generated code creates technical debt that compounds exponentially (4-month failure window, 3-week recovery timeline)
  • The 12-Factor Agents framework emerged from studying ~100 real AI engineers shipping $100K+ contracts—represents practitioner consensus, not vendor marketing
  • Newer coding models produce code faster than 2024 models, which means failure modes (like the primary key routing bug) will surface even quicker without governance
  • Loop engineering and harness engineering are emerging as critical patterns for reliable AI-assisted development workflows

Why this matters for operators: Engineering teams evaluating AI-assisted development, AI agent reliability, code quality governance, LLM application architecture

I cover AI×GTM intelligence like this every Wednesday.

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This analysis was produced using the STEEPWORKS system — the same agents, skills, and knowledge architecture available in the GrowthOS package.