Unknowns-first prompting for agentic coding

Thariq frames advanced agentic coding as an unknown-discovery problem: the prompt, plan, and context are only the map, while the real codebase, constraints, edge cases, and tacit product knowledge are the territory. Why it matters: As coding models get stronger, the bottleneck shifts from raw generation to how well humans surface missing context. Blind-spot passes, interviews, prototypes, implementation notes, and post-change quizzes can reduce hallucinated assumptions and make agent work more reviewable.

Try this: Before a complex agent task, ask for a blind-spot pass, then have the model interview you one question at a time about architecture-changing ambiguities. During the build, keep implementation notes for deviations and edge cases. Afterward, ask for a quiz or explainer before merging.