An AI-Native Workflow Gathering for Leaders in Tech

A high-signal evening summit for Founders, Engineering Leaders, CTOs and product leaders shaping how engineering Teams become next.

📍 Lisbon, Devoteam (Auditorium & Lobby) 🕙 17:00–22:00 | Monday 9 November 🤝 70 attendees (curated only)

AGENDA //

17:00 Doors open: Networking & Drinks 18:00 Democratising AI Platform Engineering with Open Source: Own It, Run It, Trust It by Alfonso Sandoval from Cisco.

AI platform engineering is usually sold as something only big teams can afford, or as a leap of faith in someone else’s black box. It shouldn’t need either. When every agent, tool and permission is open to read, run and change, a small team can start small, see how each decision is made, and decide how much freedom its agents get.

18:30 Beyond AI Coding: How Engineering Leaders Are Rebuilding Their Teams and Workflows Panel

🎤 Rijk van Zanten, CTO & Co-Founder - Directus 🎤 Pedro Afonso, VP of Engineering - Engine AI

AI is changing far more than how we write code. It's reshaping engineering teams, workflows, leadership responsibilities, and the way software gets built and shipped. Expect candid insights, real-world experiences, and practical lessons on what's working, what's not, and what comes next.

19:30 Mastermind Roundtables (topics below) 20:30 Networking, Drinks & Bites

Masterminds Roundtables

Confirmed Roundtable Leaders:

  • Gina Schinkel, Founder @ Driftawave

  • Tom Droste, CTO @ Secret View

Area 1: What Actually Changes

⚡ Junior Hiring Challenges: If AI does entry-level work, how do you hire, onboard and grow juniors, and where do your future seniors come from? ⚡ Proving the ROI: What are you actually measuring, and how do you explain AI productivity to your CEO, CFO or board without made-up numbers? ⚡ Reshaping the organisation: Team sizes, EM-to-engineer ratios, new roles (AI enablement, platform), and what the engineering manager's job looks like now.

Area 2: How We Rebuilt Our Workflow ⚡ Quality and ownership: Code review, tech debt and security when much of the code was generated. Who is accountable for code nobody fully wrote? ⚡ Rollout and governance: Getting adoption past the enthusiasts, handling resistance, tool sprawl, cost control and usage policies. ⚡ Beyond autocomplete: Agents picking up tickets, writing tests, triaging incidents. What actually works in production, and where do humans stay in the loop?