# Harness Engineering, Onboarding Bottlenecks, and the New PM Operating Model *By PM Daily Digest • May 26, 2026* OpenAI’s harness model shows how PM work is becoming more executable: PRDs, tests, and rules that agents can ship against. This brief also covers why onboarding remains a hidden growth constraint and how to evaluate feedback tools as part of a connected context graph. ## Big Ideas - **Harness engineering is becoming PM work.** At OpenAI, PMs shipped around **100K lines of production code** through PRDs, tests, docs, and harness rules instead of typing in an IDE [^1]. The harness combines an `agents.md` operating loop, a docs tree, tests and lints that encode taste, specialized review agents, and observability/computer-use checks [^1]. In one experiment, an internal app reached roughly **1M lines of code** from an empty repo with no human-typed production code; failures were addressed by improving the harness [^1]. **Why it matters:** PM leverage is shifting from handoffs to executable artifacts. **Apply it:** write PRD + acceptance tests/evals + decision docs before implementation. > "The differentiator is how much of your team’s judgment is embedded in your harness." [^1] - **Strategy still starts with the first user.** Paul Graham argues startup ideas should be framed as **idea + early adopters**; if you cannot say who will use the product when nobody else is, move on [^2][^3]. Building something you want helps because you and your peers become that first cohort [^4]. **Apply it:** add an "early adopters" line to every concept doc. - **Agentic products need human control surfaces.** The emerging pattern is software that humans and agents use together, with approval flows, summary inboxes, logs, and rollback—not just agent-only CLIs [^5]. **Apply it:** specify the human checkpoints for every AI workflow. ## Tactical Playbook - **A safer PM workflow for AI coding agents:** 1. Stress-test architecture against the PRD before coding [^6]. 2. Pull fresh docs and research first [^6]. 3. Split work into small components; quality reportedly drops past about **200K tokens** [^6]. 4. Generate tests first so the model has a target [^6]. 5. Use plan mode, manual approvals, and frequent Git pushes [^6]. 6. Restart polluted sessions; store key decisions in markdown and keep context narrow [^6]. **Why it matters:** these steps directly target common hallucination and context failures. ## Case Studies & Lessons - **Onboarding keeps surfacing as the real bottleneck.** For a workflow tool, the biggest growth lever was reducing time from sign-up to real value, especially first project setup [^7]. A separate B2B SaaS complaint shows the opposite pattern: users create a password, verify email, set up TOTP, and import a team, then later delete the password once SSO arrives [^8]. **Lesson:** design onboarding for the end state, not the temporary workaround. - **In marketplaces, vendor onboarding/catalog ingestion can be harder than vendor acquisition.** One founder said vendors liked the internal software once onboarded for orders and inventory, but uploading products, pricing, photos, descriptions, variants, delivery constraints, and categories could still take weeks [^9]. Options under consideration included concierge onboarding, internal tooling, PIM tools, POS integrations, and AI/OCR ingestion [^9]. **Lesson:** if setup takes weeks, acquisition is not the real growth constraint. ## Career Corner - **AI-native PMs need executable specs.** In one OpenAI example, a PM wrote a markdown PRD for a "skills system," the team reviewed it once, and the feature existed by week end with tests passing [^1]. **Practice:** express one feature as PRD + tests + evals an agent can run [^1]. - **For internships, signal structured thinking.** Expect product improvement, prioritization, stakeholder conflict, metrics/KPIs, basic agile, and tech-fluency questions; interviewers are judging communication, curiosity, ownership, and problem solving [^10]. **Practice:** answer aloud with a clear structure and success metric. ## Tools & Resources - **Feedback tools still split into three jobs:** analysis (**Kapiche, Unwrap, Chattermill**), collection (**Canny, Productboard**), and behavior (**Pendo, Hotjar**) [^11]. The main caution: separate collection and analysis tools force manual stitching across Slack, tickets, surveys, and widgets [^12]. A better selection principle is to favor a connected feedback graph so agents can surface patterns and PMs can spend more time on judgment [^13]. --- ### Sources [^1]: [How PMs Ship 100K Lines of Code at OpenAI with Ryan Lopopolo, Member of Technical Staff](https://www.news.aakashg.com/p/ryan-lapopolo-podcast) [^2]: [𝕏 post by @paulg](https://x.com/paulg/status/2059012311403155654) [^3]: [𝕏 post by @paulg](https://x.com/paulg/status/2059011859953410286) [^4]: [𝕏 post by @paulg](https://x.com/paulg/status/2059012638449823923) [^5]: [𝕏 post by @lennysan](https://x.com/lennysan/status/2058914803360600238) [^6]: [r/ProductMgmt post by u/InfamousInvestigator](https://www.reddit.com/r/ProductMgmt/comments/1tn2b20/) [^7]: [r/startups comment by u/tw1x_45](https://www.reddit.com/r/startups/comments/1tnoqr4/comment/onvo560/) [^8]: [r/ProductManagement post by u/Legal_case16](https://www.reddit.com/r/ProductManagement/comments/1tn75jr/) [^9]: [r/startups post by u/tonyyayo93](https://www.reddit.com/r/startups/comments/1tnj9ij/) [^10]: [r/ProductManagementJobs comment by u/Alarmed_Campaign_338](https://www.reddit.com/r/ProductManagementJobs/comments/1tnw7sk/comment/onx06ot/) [^11]: [r/ProductManagement post by u/Excellent-Garage8695](https://www.reddit.com/r/ProductManagement/comments/1tnmpf2/) [^12]: [r/ProductManagement comment by u/canhigher23](https://www.reddit.com/r/ProductManagement/comments/1tnmpf2/comment/onv80ec/) [^13]: [substack](https://substack.com/@aakashgupta/note/c-264973497)