Workshop

Building an AI-Native Engineering Team

Mihail Eric

Creator of Stanford's AI Coding Course

How to build AI-native engineering orgs from infrastructure to team adoption

Learn in 1 day what engineering leaders are spending quarters figuring out through trial and error.

There's endless content on AI coding tools, but almost none of it answers the questions engineering leaders actually face: how to reorganize the team, set up the tools, govern the risk, and control the spend.

This workshop teaches the leadership strategies beyond simply installing Claude Code licenses:

  • How to restructure teams and roles when engineers are orchestrating multiple agents

  • How to hire and interview when banning AI in interviews no longer makes sense

  • How to ship AI-generated code fast without losing quality: risk-tiered review, agent threat models, coding agent defenses

  • How to build the infrastructure layer that makes it safe: MCP portals, LLM gateways, self-improving review loops

  • How to control token spend and prove ROI with metrics

The lessons are grounded in case studies from dozens of engineering leaders running this transformation right now, at companies of all scales from leading AI startups to public enterprises. You'll learn what's worked, what hasn't, and walk away with field notes you can implement on Monday.

What you’ll learn

A focused 6-hour deep-dive on how to build the infrastructure, culture, and practices of an AI-native engineering org

  • Risk-tiered AI code review: how leading teams decide what gets auto-approved and where to keep a human-in-the-loop

  • Principles of modern software factories that allow your codebase to continuously evolve with agents

  • LLM gateways, MCP portals, and strategies for controlling what your agent can touch

  • Reference architectures with real technology options, from managed platforms to open-source assemblies

  • Org patterns from the field: pod structures and the new organizational structure of platform and product teams

  • The culture mechanisms that actually drive adoption, and why tool access alone moves nothing

  • How to interview and hire for AI-native skillsets

  • All about the governance spectrum that enables cost-effective team-wide coding agent use

  • When frontier models earn their cost and when you should consider open source models

Workshop agenda

  • [Module] Building Fast & Safe With AI Coding

    The agent threat model, risk-tiered AI code review, MCP portals and LLM gateways, self-improving review loops, and the path from ad-hoc tool use to a software factory.

  • [Breakout] Review, Security & Infrastructure

    Split up into groups with fellow engineering leaders and conduct guided discussion and field learnings on workshop topics

  • [Module] Organize & Staff the AI-Native Team

    What AI-native actually means, org structures beyond the pod model, culture mechanisms that drive adoption, and how hiring and interviewing change when engineers lean on AI.

  • [Breakout] Roles, Hiring & Culture

    Split up into groups with fellow engineering leaders and conduct guided discussion and field learnings on workshop topics

  • [Module] Cost & Proving Impact

    Token spend governance, model economics, the metrics stack that shows AI-native is working, and ROI framings that survive a CFO conversation.

  • [Breakout] Spend, Metrics & the ROI

    Split up into groups with fellow engineering leaders and conduct guided discussion and field learnings on workshop topics

  • Wrap: Your 30/60/90 Plan

    Commit to concrete next steps using workshop materials for the next 30/60/90 days

Learn directly from Mihail

Mihail Eric

Mihail Eric

Contact

Created Stanford's first AI coding class. Former YC founder and Amazon AI lead.

YC, Amazon, Monaco
Y Combinator
Amazon
Stanford University
See all products from The Modern Software Developer

Who this workshop is for

  • Eng leaders (Directors, VPs, CTOs) who are delivering an AI transformation and need proven org structures, governance models, and ROI

  • Senior, staff, and principal engineers who've mastered coding agents personally and now need to scale that into team-level workflows

  • Platform and DevEx leads who own the tooling layer and must make the build-vs-buy calls on AI infrastructure

What's included

Mihail Eric

Live sessions

Learn directly from Mihail Eric in a real-time, interactive format.

Lifetime access

Go back to course content and recordings whenever you need to.

Community of peers

Stay accountable and share insights with like-minded professionals, including lifetime access to an online community of engineering leaders guiding AI transformation at different organizations

Certificate of completion

Share your new skills with your employer or on LinkedIn.

The AI-Native Readiness Scorecard

A self-guided assessment for evaluating your organization's AI-native readiness

30/60/90 Action Plan

Create concrete actionable strategies to implement in your team for the next quarter and beyond

Reference Architectures for the AI-Native Stack

Sample architectures and technology choices for every key infrastructure component in an AI-native stack

Maven Guarantee

Your purchase is backed by the Maven Guarantee.

Frequently asked questions

Maven for Teams

Reimbursement

Get your company to pay

Everything L&D needs: email template, receipts, and certificate of completion.

Get reimbursed

Team discount

Learn with your teammates

Save 20%+ when 2 or more teammates enroll in the same cohort.

Save 20%+ with a team

Private cohort

Run a cohort for your org

A dedicated cohort with a custom schedule and curriculum, tailored to your team.

Book a private cohort

$750

USD

Sep 12
·

12–6pm EDT

Enroll