4 articles in Architecture
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.
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.
AI coding agents don't understand your architecture — they pattern-match from your codebase. One boundary violation becomes a template that AI replicates hundreds of times. Clean Architecture went from best practice to survival requirement.
A new framework argues that coding agents need external "harnesses" — feedforward guides and feedback sensors — to produce reliable code. We agree. We also built one. Here's how theory maps to a production system, concept by concept.