17 articles in Engineering Practices
Most developer tools fail adoption not because they're poorly built, but because they're designed from the wrong starting point. Here's how Jobs-to-be-Done thinking explains why engineering tools succeed or sit unused — and what it reveals about AI adoption.
Every new model release prompts the same question — is this the one that finally makes AI coding agents reliable? It's the wrong question. What keeps single-agent workflows from scaling to production is architectural, not about the model. Here's the pattern the teams shipping real code keep converging on.
AI coding agents are the most powerful tools engineering has ever had. But the teams getting unreal results aren't just prompting — they're combining AI with practices that unlock outcomes nobody else can explain. Hypothesis-driven development is the first unlock.
Harness engineering gives coding agents guides and sensors to make output reliable — the downstream controls that ensure things are built right. Shape is the upstream control that ensures the right thing is being built. Here's what the shape discipline looks like — and why AI teams that skip it ship well-built fragments of the wrong product.
Gene Kim and Steve Yegge argue that vibe coding works for production — if you have preventive, detective, and corrective controls. We agree. We built the platform that enforces those controls structurally. Here's what we've learned about why practices alone aren't enough.
Most teams treat engineering quality reactively — ship fast, catch problems in production, fix when it hurts. That cycle keeps you busy without making you better. Prevention, Detection, and Correction form a closed loop where each phase reduces the work the next must do.
Your dashboards show green. Your test coverage is stable. But three threats are compounding underneath — defects, debt, and non-determinism. AI is making each one worse, faster. Here is how to see them before they see you.
The cost arbitrage that made outsourcing attractive has flipped. Three disciplined engineers with AI agents now outship 15-person outsourced teams — at a fraction of the cost, with measurably higher quality.
Most engineering transformations fail not because the team lacks skill, but because they defer improvement to a rewrite that never finishes. Here is how the rewrite trap works, why it compounds, and what to do instead.