35 articles in AI Engineering
AI coding agents are multipliers. Multiply zero, get zero. Multiply a typical outsourced codebase, and you don't accelerate delivery — you accelerate rework. Here's the founder math on why your AI ROI is landing on the wrong substrate.
Even a good hypothesis needs a map before it needs code. AI agents build whatever ticket lands in front of them — they can't see the journey the ticket sits inside. User story mapping is the practice that turns a backlog of features into a walking skeleton of the user's journey. It's the second unlock.
Engineering resistance to AI tools is not a change management problem. Every complaint maps to a specific missing prerequisite. Here is how to read the resistance as a diagnostic — and the five-stage sequence that resolves it.
Most founders evaluate engineering vendors on story points and velocity. Neither metric predicts whether software reaches customers. Here are the four questions — no technical background required — that reveal everything.
Your PM writes prose. Your engineer translates it to code. QA finds the gaps. Your AI agent just compressed this broken loop from weeks into hours — without fixing any of the misalignment. Specification-driven development is the contract layer both sides can read, write, and execute. And it's the practice that separates teams who get leverage from AI agents from teams who ship the wrong thing, faster.
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.