Long-form essay by **Shubham Saboo** (X/Twitter) advancing a thesis on the Product Manager role in the age of agents: the next key skill is **not prompt engineering** but **Loop Engineering** — designing a *system that improves with every run* rather than writing the perfect prompt every time. A **loop** is a repeated cycle: change what shapes the agent's behavior → run it → evaluate the output → keep the change if quality rises, revert otherwise → **compound the learning** so the next version starts ahead. For a PM, the entry point is not code but the **durable artifacts** that encode their judgment: PRD-review skill, customer-call *summarizer*, evaluation rubric, launch checklist, research workflow, `CLAUDE.md`, prompt template, prioritization framework. Because they are reused, these artifacts **compound in both directions** — and **drift** silently (a CLAUDE.md that keeps growing, a checklist that gets ignored…): the model has not regressed, the artifacts have drifted unwatched. A loop has **5 parts**: trigger, action, **proof**, memory, **stop condition** (the most critical). **Evals** become PM work (testing the artifact against known examples: 3 good / 3 bad PRDs, 5 understood calls, 2 past launches). **Memory** lives on **GitHub** (the repo becomes "product memory": commits, diffs, eval results, decision log, rollback). Recommended first loop: a **weekly product signal loop** (every Friday). Taste remains central — but it now needs **proof**. Cites Boris (creator of Claude Code): "he no longer writes prompts, he writes loops."
Google whitepaper (the "Day 1" installment of a series, by Addy Osmani, Shubham Saboo and Sokratis Kartakis) that maps the transformation of the software development lifecycle (SDLC) in the era of coding agents. Thesis: the fundamental shift is not a new language but the move from writing code to **expressing intent**. The document lays out a spectrum from *vibe coding* (prompting and accepting) to *agentic engineering* (AI implements under constraints, tests, and feedback loops designed by humans), with **context engineering** as the central skill, the **software factory** model (the developer's deliverable = the system that produces the code), **harness engineering** (Agent = Model + Harness), and a CapEx/OpEx economic analysis of total cost of ownership.