Article: Comprehension as an Architectural Characteristic: A System That Is Not Understood Cannot Evolve Safely

The article highlights a shift in how code is produced: AI tools now generate large portions of code, which bypasses the traditional learning process developers undergo when writing code themselves. This change means the implicit knowledge that once accumulated as part of the system’s architecture is no longer guaranteed to exist. As a result, the system’s “theory”, the mental model held by its creators, can disappear unless deliberately preserved.

Because comprehension is not directly measurable, the authors suggest watching for indirect signals of decay, such as increased time spent on incident calls to understand system behavior. Teams must allocate effort to maintain a shared understanding, for example by conducting regular human reviews that act as comprehension checkpoints rather than mere quality gates. This adds a cost in terms of time and coordination but protects against hidden cognitive debt that can cripple future changes.

The article warns that without a collective model of both the “what” and the “why” of a system, evolutionary architecture loses its safety guarantees. When knowledge concentrates in a few individuals or becomes fragmented across decentralized teams, the global view of the system erodes, making large-scale evolution risky. Engineers should therefore invest in practices that disseminate the system’s theory across the whole team, ensuring that any future modifications are made with full awareness of existing intent and design.

Adopting the recommended practices means rethinking existing workflows: code generation must be preceded by a comprehension step, and human review must be treated as a knowledge-transfer activity. This may require new tooling to surface decay signals and structured documentation to capture intent. However, if these steps are omitted, the system’s ability to evolve safely will be compromised, especially as reliance on AI-generated code grows.