The author proposes a new methodology for software development in the era of neural agents, emphasizing the need to adapt traditional practices like Test-Driven Development (TDD). To make TDD effective with AI, developers must design architectures that support automated testing, such as decoupling input layers to allow synthetic inputs.
For graphical applications, the author suggests using headless modes and synthetic inputs for validation. Neural models can be utilized to analyze screen outputs, such as through pixel-delta comparisons or direct screen reading, while automated test pipelines can run continuously, including during off-hours.
Knowledge management for AI differs from human documentation. AI requires structured, hierarchical data to avoid hallucinations, whereas humans prefer stylistic narratives. The author recommends creating a project-specific 'AGENTS.md' file that acts as an index for agents to retrieve necessary information efficiently.
The concept of an 'invariant network' is introduced to improve AI performance. By defining explicit, well-named rules and standards, developers can constrain agent behavior. These invariants should be clearly documented to prevent agents from bypassing formal restrictions through casuistry.
The author warns against the loss of manual coding skills due to over-reliance on AI. To maintain architectural and design proficiency, developers are encouraged to use AI as a tool for creating educational materials, exercises, and self-assessment tasks to keep their skills sharp.
