TechLead Conference 2026 Amsterdam

2 years ago, the engineering team's structure was fairly standardized, 2-pizza cross-functional teams and an engineering manager leading the team. These days, everyone is figuring it out as they go, making adjustments and seeing what may work and what not.In this talk, Gregor will share how companies like OpenAI, Anthropic, Shutterstock and others build engineering teams in the AI era. He will also share his recommendation on the best structure for a specific type of organization.

AI is giving developers unprecedented autonomy. Every engineer can now create their own workflow, combining coding agents, prompts, knowledge bases, and tools in different ways. While this can dramatically increase individual productivity, it introduces a new challenge for engineering leaders: maintaining consistency across teams.In this talk, we'll explore how AI is creating new forms of organizational drift, why team alignment matters more than ever, and practical approaches for building share

We used to explain our bugs to rubber ducks. Now, the duck talks back, writes code, and occasionally lies to our faces.LLMs are incredible tools. They can cure "blank screen syndrome," instantly write boilerplate, and explain weird legacy code. But treating them like senior engineers is a massive trap.

Our telemetry dashboard was green — a bug-fixer skill firing hundreds of times a week, a test-coverage agent running on every diff, manual-tester agents driving browser test cases across products, security reviews kicking off across teams, daily active users climbing. And I still couldn't answer one question: did any of it make us better? Every usage metric was lying. This talk is how we drove real adoption of an internal AI plugin marketplace across 30+ engineering teams — hands-on workshops an

Teams often start agent adoption by adding tools: MCP servers, API wrappers, chat interfaces in developer portals. But once coding agents move from local help into real delivery workflows, they usually fail earlier than the tool call. They read stale ownership, conflicting runbooks, missing deploy history, and policies that were written for humans.This talk introduces company memory as the missing layer for production coding agents: trustworthy, permission-aware engineering context that agents c

AI makes individual engineers faster - and quietly more isolated.As coding agents become everyone's default pair, the collaboration that keeps an org healthy erodes: pairing fades, context gets trapped in private chat histories, knowledge stops diffusing, teams drift into silos.Velocity dashboards look great, so nothing seems wrong - until onboarding slows and bus-factor spikes.This talk names the failure mode, explains why standard metrics miss it, and shows what leaders can do to keep teams co

AI usage grows, cost follows, and leadership asks you to control it. The instinct is to reach straight for a spending cap. Our concern was that doing so would undermine the adoption we had encouraged, while the arithmetic showed that a daily limit would not control the overall budget anyway.This case study follows the design of cost governance for AI development tooling across an engineering organisation while adoption continues to evolve. Governance that has any chance of sticking depends as mu