What is AI-directed engineering?
AI-directed engineering uses AI for implementation speed while the developer retains direction, review, testing, and accountability.
Governed catalog
Practical notes from systems, operations, data, security, and AI-directed engineering.
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AI-directed engineering uses AI for implementation speed while the developer retains direction, review, testing, and accountability.
Multiple AI models can be used for complementary development and verification roles while the system remains model-neutral.
A model-neutral workspace keeps durable project context in files rather than binding the work to one AI provider.
AI agent continuity uses files for pickup, session state, durable memory, and formal handoff evidence.
Lost-Agent Recovery restores a work session from governed files instead of relying on missing chat history.
Durable memory holds stable facts while session state holds the current execution checkpoint.
A source-of-truth hierarchy makes the authoritative document or runtime artifact explicit when evidence conflicts.
Owner gates keep consequential AI-assisted actions explicitly human-authorized.
Keep credentials out of AI context and repositories; use protected configuration and explicit access boundaries.
Completion requires recorded verification evidence that matches the risk and scope of the change.
Classify platform, environment, workflow, and application failures separately before repairing code.
Inspect and reuse existing architecture and components before introducing new code.