How I Shipped 2 Months of Features in 3 Weeks With LLM Agents (and What It Means for Engineering Leaders)

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Thomas Walter : Partner & CTO HealthTech

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In three weeks, I pushed 15 brand-new features into production for ComPaRe EMA, the mobile app we’re building for AP‑HP.

Without LLM agents, that same backlog would have taken two months to develop.

Without AI the team estimated 2 months-dev needed to ship to production the 15 features left

This isn’t about typing speed. It’s not “Copilot writes code so I don’t have to.”

The real shift is a new way of designing, coding, and managing software projects — what I call *Right-First-Time Agentic Coding. (*a bit longer than “Vibe planning”). In this model, conception dominates completion: you engineer the whole flow with an AI agent before you write a single line of code.

As a CTO, I deliberately stepped back into the role of developer on this project. Because you can’t manage, train, or make strategic calls about AI-assisted development if you haven’t lived through the details yourself. Buying Copilot licenses for everyone and “waiting to see” is a recipe for mediocrity. There is no better way to understand the future of coding, than by experimenting yourself.

ComPaRe EMA is capturing contextual insights on daily living with a chronic disease to foster research.

Let’s talk about productivity

“AI productivity” stats are often illusionary, they measure speed, not value.

Take for instance the headline that “AI boosts commits by 17 %”.

If your customer stills receives the same app on the same date with identical quality, that 17% is a 0% productivity gain.

In fact, the illusion of faster typing can backfire: studies show that teams who rely on AI for “speed” sometimes experience **longer lead times** on real Open Source repo issues.

According to the lean tech manifesto, what matter most is the Value for the Customer. So in lean terms productivity = value delivered ÷ cost.

For my customer (AP-HP), value means:

Lead time — do they get their app faster to start research earlier ?
Better research insights — does the app collect more diverse or better data (less bias, better UX, stronger performance), so researchers can advance their work faster?

For Theodo, cost translates to man-hours and resource allocation needed to achieve same quality.