The AI Coding Adoption Ladder & The MCP Wire

We are thrilled to welcome you to our 11th edition of AI Native Netherlands, hosted at the Studytube headquarters in Amsterdam.

In this edition, Pini takes the organisational view of AI coding adoption - how it progresses across an engineering org and the order things tend to break as it scales. Aleksei goes hands-on into the tooling - what happens when you build an MCP server the way the docs suggest, where it falls apart, and the pattern that replaces it.

A massive thank you to our host, Studytube, for their generosity in providing the venue and drinks for this evening and for supporting the Dutch AI engineering community.

We'll cover:

• How AI coding adoption progresses across an engineering org, from first licences toward a mostly automated delivery process
• The order things usually break as adoption scales: coordination, review, testing, context, security boundaries, ownership, CI/CD, and product flow
• Why faster individual developers don't automatically mean faster team delivery
• The platform and infrastructure foundations for scaling AI-generated code safely
• Where traditional MCP tools fit, and where they hit their limits
• Giving a model an API plus a sandboxed code environment instead of writing a new tool for every use case
• Running model-generated code safely: sandboxing, module allowlists, CPU and memory limits, static analysis, and audit logs

Speaker 1: Pini Reznik (re:cinq)
Pini Reznik is a seasoned technology leader with extensive experience in the software industry. He is the CEO and Co-Founder of re:cinq, experts in Cloud Native and AI Native engineering. Former software engineer, Co-Founder of two successful consulting businesses and an O’Reilly author.

Talk: The AI Adoption Ladder: From First Licences to the Dark Factory
AI coding tools make individual developers faster, but many teams discover that delivery does not improve at the same rate. PR volume rises, review queues grow, tests become less convincing, context gets messy, and eventually product and platform bottlenecks become visible. AI did not remove the constraint; it moved it.

This talk introduces the AI Coding Adoption Ladder: a practical model for how engineering teams move from first licences to structured adoption, agentic workflows, and eventually software factory patterns. We will look at the order things usually break - coordination, review, testing, context, security boundaries, ownership, CI/CD, and product flow - and what software and DevOps engineers need to fix before AI-generated code can safely scale.

Speaker 2: Aleksei Moiseev (Studytube)
Aleksei is a software architect and engineer with 20+ years of experience building production systems, from hands-on development to large-scale architecture and platform engineering.

Today, he focuses on agentic AI, designing software factories where LLMs and autonomous agents perform real engineering work. His work spans self-hosted AI infrastructure, multi-agent systems, and the architectural patterns needed to run them reliably in production.

He builds and operates the systems he writes about, with a focus on practical, resilient engineering over hype.

Talk: The MCP Wire
Build an MCP server the way the docs tell you to — one API endpoint, one tool — and it works. Then you ask it to create 1,000 users and wait 25 minutes while it makes 1,000 separate calls. Multi-step workflows turn into long chains. Every new use case is another tool to write, test, and maintain. Adding "bulk" tools just moves the problem around.

Aleksei, an AI Solutions Architect at Studytube, walks through building exactly this server for Studytube's user and team management API, hitting that wall, and arriving at a different pattern: give the model an API and a sandboxed Python environment, and let it write and run code for the task in front of it. Ten tool calls become one short loop, executed once — about two seconds instead of fifteen.

The talk covers where traditional MCP tools still fit (simple, atomic, read-only operations), where code execution takes over (batch jobs, conditional logic, multi-step workflows), and how to run model-generated code safely: sandboxing, module allowlists, CPU and memory limits, static analysis, and full audit logs.

A hands-on talk for anyone building with MCP, or about to.

Agenda:
18:00 — Arrival, food & drinks
18:45 — Talk #1 | Pini Reznik
19:30 — Short break
19:45 — Talk #2 | Aleksei Moiseev
20:30 — Networking & more drinks
21:00 — Wrapping up

What to bring: Just curiosity and your own questions. If you're wrestling with agentic workflows, CI/CD under AI-generated code, or security boundaries as adoption scales — or if you're building with MCP and hitting its limits — bring those.

Who is this for: Platform engineers, AI/ML engineers, SREs, architects, and engineering leaders working on reliable AI systems in production — and anyone building with MCP, or about to.

Where to find us: Studytube, Danzigerkade 17, 1013 AP Amsterdam — Google Maps Link