Speaker
Abstract
Early-career engineers are learning the craft in an environment where the surface they touch is often an agent, not a codebase. The instinct is to treat this as a rupture in how engineers grow. It isn't. The principles that grew engineers before agents still hold: gradually widening the scope of what someone owns, pairing to transmit judgment that doesn't fit in a doc, feedback loops tight enough to build intuition, real exposure to production. Those are how humans learn a craft. What's changed is the mechanics underneath each one, and the mechanics are where growth can stall.
Scope no longer means "size of the diff." Pairing no longer means "two people at one keyboard." The tightest feedback loop in a junior engineer's day is now the loop between them and their agent, and the shape of that loop is doing more of the teaching than most teams have noticed. Apply the old principles through the old mechanics and you get engineers who ship fast for a year and then plateau, or worse, look senior on the surface and can't hold up under a real incident.
Drawing on how we've been growing engineers inside Netflix through this transition, I'll lay out a working framework: what to keep, what to rebuild, where the failure modes hide, and how to tell the difference between an engineer who's actually growing and one who's just moving fast.
What you'll leave with:
- A concrete model for scoping work to a growing engineer when the agent is doing the typing.
- A rebuilt definition of pairing for the agentic loop, and where it belongs in a team's week.
- The failure modes that look like productivity and aren't, with signals to watch for.
$2,955, Conference (3 days). Current pricing ends October 13th. All pass options.
QCon San Francisco 2026 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
Part of the track
Engineering the Developer Experience Hosted by Ankit Jain Co-Founder & CEO @AviatorFrom the same track
Wednesday 18 November
10:35 Ballroom A Session Features vs Futures Kent Beck Original Signer of the Agile Manifesto, Author of the Extreme Programming Book Series, Rediscoverer of Test-Driven Development Details coming soon. 11:45 Ballroom A Session Software Engineering No More Small Systems, How AI May Bring Large System Problems to Everyone Adam Bender Technical Lead for Enterprise Engineering @Google Once you figure out how to use AI to make your organization productive, you will soon find you have a whole new set of problems. While some of those problems will be novel thanks to LLMs, many of them are just the result of there being "more stuff". 13:35 Ballroom A Session Growing Engineers in the Era of Agentic Coding Adam Berry Staff Engineer @Netflix Early-career engineers are learning the craft in an environment where the surface they touch is often an agent, not a codebase. The instinct is to treat this as a rupture in how engineers grow. It isn't. 14:45 Ballroom A Session Software Understanding Shipping Faster, Understanding Less Margaret-Anne Storey Professor of Computer Science @University of Victoria, Co-Creator of the SPACE Framework and the Triple Debt Model for Software Health, a Canada Research Chair in Human and Social Aspects of Software Engineering AI is changing software development faster than our engineering practices are evolving, and yet many teams are facing growing pressure to ship features faster. But moving faster can create three related threats to software health: technical debt, cognitive debt, and intent debt. 15:55 Ballroom A Session The Diary of an AI Adopter: How I Went From Skeptical to Enthusiastic in Our Organization’s AI Migration Efforts Tara Hernandez Vice President @MongoDB & Lecturer at the UC Santa Cruz Center for Innovation and Entrepreneurial Development From market research reporting questionable success of AI adoption in enterprise, to CEOs announcing layoffs due to AI, to companies announcing they’re cutting back or eliminating AI due to prohibitive costs or technical missteps, it can be hard to know what the path to success should look like.