Mark Huebel · VP of Engineering, TimelyCare
Build speed is solved.
Absorption isn't.
I ran the experiment at TimelyCare, where I lead engineering. Moving product development to AI‑native delivery raised build speed roughly 30‑fold against our 2022 baseline — and delivered output about four‑fold. The gap is work the rest of the organization couldn't absorb: review, release, go‑to‑market. The writing here is about what to do once the constraint leaves engineering.
- 27×
- concept to production
- 36×
- time to first executable
- ~4.6× per engineer
- team 16% smaller
- 0.90 → ~0
- defect rate
The figures, and where they came from
Median time from concept to production fell from about 20 days to 18 hours, measured against the 2022 pre-platform baseline. Time to first executable came down about 36-fold over the same period. The defect rate went from 0.90 to near zero, recording zero in four of the last six months.
Resolved engineering work went from 142 issues in a 2022 quarter to 551 in a 2026 quarter, while the organization went from 19 people to 16. Build speed rose far faster than delivered output — the difference is work the surrounding ~240-person organization could not absorb, which moved the company's constraint out of engineering.
A product manager working directly with agents delivered in 47 days a case-management capability a pod had spent 136 days approaching — and showed along the way that the company could retire a third-party contract rather than integrate it, ending a recurring licensing cost.
Measured against our 2022 pre-platform baseline. Duration figures from internal delivery metrics; work volume from resolved engineering tickets.
Where the writing comes from
I'm a self-taught engineer. I started out freelancing — an inventory system for a nonprofit, a job tracker for an engineering firm, apps for a smart-sprinkler company later acquired by Moen — then joined Stack Sports, where I went from mobile developer to leading new projects within a year.
In 2019 I joined TimelyCare as its first engineer, before there was a production product. I built the core of the telehealth platform, grew the engineering organization as the company scaled, and lead it today as VP of Engineering.
The writing here comes from the most recent chapter: moving product development from human-driven to substantially agent-driven, in regulated healthcare, and keeping notes on what held up. The doctrine lives in Harness Engineering; the blog is the working notebook.
The operating doctrine, written down
After changing how TimelyCare ships, I wrote the operating doctrine down — five chapters distilled from that practice.
The Strategy Compounding Loop
Separates execution, alignment-checking, and strategy revision, so the work itself sharpens the strategy that steers it.
The Agentic PDLC
Moves product, design, and engineering from prescribing the solution to steering the agent: the decision-space value curve, and the seams principle for spec design.
The 9-Stage AI Adoption Model
The organizational roadmap from traditional delivery to AI-native, used to sequence the transformation pod by pod.
Loop & Harness Engineering · five chapters, each a loop
01 The Loop · 02 Inside a Build · 03 Scaling to a Portfolio · 04 Betting Beyond the Data · 05 About This Doctrine
Read the doctrineWhere this goes next
I write about building with AI agents at production scale without losing the wheel. If you're working the same problem, I'd welcome the conversation.