Most of what I ship is written with agents. That changes what my job is, so here is the honest division of labour.
I write the objective and the acceptance criteria it has to meet. An agent drafts the plan against them, and I review that plan before any code exists.
Agents write most of the code. The interesting question stopped being who typed it and became whether the constraints were right.
Reviewers start from a clean context, sometimes on a different model family, so they do not inherit the implementer's assumptions.
Tests and runtime checks decide whether something works. A confident summary from a model is not evidence.
I approve plans, and anything irreversible stays mine: what gets published, and what gets privileged access to a live system.
When something goes wrong twice, the fix belongs in the process rather than only in the ticket. That is where most of the compounding happens.
I lead the development team and own the architecture decisions behind the company's AI capability. The way we work with agents arrived in steps. First Cursor wired into Jira. Then Claude against that same Jira. Now Claude against WBTickets, which replaced Jira and Confluence outright. The team works one shared way across it: how a ticket gets written, how the code gets made, how it gets reviewed.
I don't treat coding agents as faster autocomplete. The work is the system around them: context that outlives a chat, a plan reviewed before any code exists, review from an independent context, and executable evidence instead of a confident summary. The goal was never to make one engineer faster. It is that the team can take on work that used to need constant senior attention, and that one improvement to the harness lands on every ticket after it.
97 colleagues at a 350-person company used the MCP platform in the 90 days to August 2026. That undercounts it, because it only sees the people who reached live company data through MCP. None of it spread on its own. We picked champions. We started with a small group and trained people as the rollout widened.
I run Claude training across the company, covering prompting, connectors and MCP, and skills. With the development team I went further and guided them into AI-first development. Colleagues each took a personal project end to end with AI, which is what actually built confidence and experience.
I coach developers through collaborative code reviews and onboard new team members. When we hired our senior AI developer I wrote the job description and the technical assignment, and sat the interviews.
Building production AI systems: from MCP architecture connecting LLMs to enterprise data, to ML pipelines on GCP Vertex AI.
18+ years in software engineering, with deep experience building enterprise web applications in Angular. Architecture, performance, and team leadership at scale.
End-to-end delivery across Firebase, GCP, .NET, and Java Spring Boot, with CI/CD, infrastructure, and DevOps.
None of these is a side project on its own. Each is a layer of the same platform: identity-bound tools over the business systems, durable organisational knowledge, workflows an agent can read, explicit approval points, and an engineering system that people and agents share. Building it that way is the point, because every layer makes the next one cheaper.
I'd been deepening my AI development practice for over a year, from Copilot tab completion, to ask mode, to fully agentic workflows, before joining Warmtebouw. Here that experience met a real opportunity: building the company's entire AI capability from the ground up. I've built eleven production MCP servers (AFAS Profit ERP, Autodesk BIM, fleet management, energy monitoring, construction standards, pre-order calculation, and a multi-million-item product catalogue), totalling more than 90 tools across eleven external APIs, secured with Microsoft Entra OAuth and RBAC. The servers let non-technical colleagues query live business data through natural language without ERP expertise. I also designed what I believe is one of the first production MCP Apps in Angular: interactive tables, charts, and maps that render directly inside AI conversations. In parallel I'm architecting a reusable MLOps platform on GCP Vertex AI, starting with email classification as the foundation for future ML models. Day-to-day I develop AI-first with Claude Code.
Full ML lifecycle management: Vertex AI training jobs, BigQuery data pipelines, model registry, and admin portal. Built as a reusable foundation for future ML models.
An AI-integrated workflow board for humans and agents. Plans, review and approval live in Markdown and Git beside the code, so the state survives when an agent's context is thrown away. It replaced Jira and Confluence, and aggregates tickets from several GitHub repos through a GitHub App, behind Entra SSO.
Employee fitness registration app with calendar-based scheduling, trainer management, and role-based access. Microsoft Entra SSO, 90% test coverage enforced.
An inline Tetris game served through a custom MCP server, with a company-wide leaderboard and AFAS-based role lookup. Built to explore MCP App surfaces beyond plain tool calls.
Led the full-stack development team (frontend and backend developers) building a unified broker portal from scratch for Allianz Netherlands. Took ownership of architecture decisions, database modeling, PO alignment, and scrum facilitation. Delivered an Nx monorepo with a shared Angular component library, Storybook, CI/CD pipelines, and a Java Spring Boot BFF with multi-language support.
Warmtebouw
HVAC workforce management system planning 100+ engineers across six departments. Syncfusion drag-and-drop scheduler, Google Maps with live vehicle tracking, conflict detection, time registration, and bidirectional AFAS Profit ERP sync via REST.
Pro-Fa Automation
Configurable dashboard platform connecting industrial data sources, enabling operators to visualize real-time production data and create custom scripting workflows.
NS (Nederlandse Spoorwegen)
Contributed to NS's high-traffic e-commerce platform serving millions of daily ticket transactions. Optimized the sales funnel through A/B testing with Optimizely, directly impacting conversion rates.
KLM Royal Dutch Airlines
Further development of KLM and Air France's aftersales web platform, delivering new features with Angular and a GraphQL BFF architecture within a complex multi-brand IT landscape.
Allianz
Led the full-stack development team building a unified broker portal from scratch. Architecture, database modeling, PO alignment, scrum, Nx monorepo, shared component library, Storybook, and Java Spring Boot BFF.
Bolster Safety
Transformed a legacy AngularJS e-learning platform into a modern Angular application with full UI redesign and management dashboard.
Teamcoda
Cross-platform Ionic mobile application for ebooks and audiobooks.
Softpak
Web application platform with meta-driven architecture that dynamically generated pages from JSON definitions for Progress OpenEdge.
OSIsoft Hackathon
Custom data visualization component for industrial analytics. Won "Best in Show" at the OSIsoft Visualization Hackathon.
OLAM
Real-time dashboard measuring Overall Equipment Effectiveness of a cocoa factory with SignalR streaming.
Total E&P
Custom D3.js dashboard components for visual control of operating envelopes of industrial machinery.
Shell / NAM
Dashboard for gas sample and metering data across multiple production sites.