Teach Your AI to Think Like a Senior Engineer (every.to)

The author lays out eight concrete strategies for training AI coding assistants to “think like a senior engineer” by running parallel, specialized research agents that plan before they code. The centerpiece example: a research agent tasked with designing a bulk‑archive (“email bankruptcy”) flow discovered Gmail rate limits, silent failures, and timeouts that turned a presumed quick feature into a three‑day architectural problem—proof that planning saves wasted implementation effort. The approach runs multiple agents in parallel to gather logs, API limits, upgrade guides, code patterns, and git history, then distills findings into a single plan you judge and refine. Technically, the workflow defines fidelity levels (Fidelity 1–3 for small fixes to unknown large features) and agent roles such as “reproduce and document” (uses AppSignal logs to build repros), “best‑practices researcher” (web + blog + docs), “codebase searcher” (finds existing patterns), “library source reader” (inspects gems like RubyLLM and test suites for undocumented features), and “git historian” (surfaces past decisions). These agents compound knowledge by saving docs/*.md, updating automated reviewers’ checklists, and auto‑refreshing when dependencies change. For AI/ML teams this signals a shift toward agentic developer workflows that automate discovery, reduce integration risk, and encode institutional knowledge—practical patterns and GitHub‑ready agent templates are provided to accelerate adoption.