Blog

The Data Dilemma, Part 1

When AI agents fail in a company, it is rarely the model’s fault. The real problem is the data: semantics, quality, and context are written down nowhere, they live in the heads of the domain experts. Data Mesh addresses exactly this problem, but it organizationally overwhelms many mid-sized companies. This first part describes why that is and derives a Minimum Viable Data Mesh from it. The second part shows the implementation in four layers.

How do you train people on a subject that changes every week?

For the past year, we’ve been training development teams to use AI agents across the entire software development lifecycle. In that time, we’ve delivered 47 trainings to more than 500 participants. Here’s what goals companies bring to us, what sets our training apart, which moments have stuck with our trainers, and why the real work starts after the training ends.

Indecent Exposure

Your SPA’s API Is Showing Too Much

What ECST Events, Domain Events, and ECST Really Are

“ECST Event”, “Domain Event”, “ECST”: these terms appear in almost every discussion about Event-Driven Architecture and Domain-Driven Design. Surprisingly often in practice, different people have a different understanding of them.

Trust but Sandbox

Coding agents can now do (almost) everything. They read and write every file you have access to, they run arbitrary commands, talk over the network, read your environment variables, and drive your browser with all your cookies. In the spirit of an agentic workflow, where we let agents work as autonomously as possible, that is actually the point, because it is exactly what makes them useful. Even so, we should find a way to gain more control over what happens in there.

Beyond Claude & GPT

How we use open-weights models for chat, coding agents, and production workloads

Stop Using Bearer Tokens Like House Keys

You’ve built an API. You protected it with OAuth 2.0. You’re using JWTs. You feel secure. You’re not.

Alternatives to U.S. Artificial Intelligence

Claude and GPT have transformed the way I write software, but I’m becoming increasingly uncomfortable with rising costs, vendor lock-in, and our growing dependence on a handful of U.S. providers. After exploring local models and open-weight alternatives hosted in Europe, I was surprised to discover that some lesser-known models can already handle much of my day-to-day work.

Faster Is Not Better: On Experimentation Culture and What It Costs

OpenProject: A Real Alternative to Jira?

In his article “Using Jira Data Center with AI Features”, my colleague Nicolas Inden showed how to connect a self-hosted Jira instance with an AI model. One point stands out: Atlassian will only support Jira Data Center until early 2029. After that, support ends. Sales of Data Center licenses to new customers were already discontinued at the end of March 2026. So what do we do if we don’t want to return to Jira Cloud after 2029 - or want to move away from Jira right now?

The Agentic Trio

The Product Trio separated discovery from delivery because delivery required a larger team. Agentic development removes this constraint. That changes what a product team can look like.

RAG and Fine-Tuning

Stronger together

Using the space, not optimizing the treadmill

Features per unit of time is not a meaningful metric. What counts is whether they satisfy a real user need and contribute to a desired outcome. Agentic development opens up the space to finally take that seriously.

The Missing Half of Your Data Strategy

In order to stay competitive, a modern data architecture is essential. It enables sound decisions and data-driven development at all levels, from strategic choices to feature prioritisation and buy-or-build decisions. The ability to collect, understand and act on data is no longer optional. Many organisations recognise this and are investing in data mesh initiatives and similar approaches. Through modern infrastructure and self-service platforms, they aim to democratise access to corporate data. However, these platforms often fail to fulfil their potential because people still do not know how to work with data or what value they can derive from it. For a successful data strategy, this has to change. Let’s talk about data literacy.

Nebu: Self-made sovereignty

How I’m building an enterprise chat server with agentic development that simply doesn’t exist.

Accidental Complexity in the Heart of Software

Domain-Driven Design went mainstream over the last ten years – in parallel with a wave of digitalization and modernization projects. That is not a coincidence. And it explains a lot about why so many of these projects fail to deliver what they promise.

Hail Mary: Why domain knowledge cannot be extracted from experts

AI agents promise to replace weeks of requirements workshops with hours of structured interviews. The pitch is compelling. The cognitive science is not.

From Vibe Coder to Agentic Engineer

How to Take Ownership of AI-Generated Code

The Right Kind of Hard

More structure, more output, more exhaustion. Spec-driven development has helped me get better results from AI agents – and drained my energy in the process. About dead weight, false clarity, and hidden costs I didn’t plan for.

AI Features for Jira Data Center – No Atlassian Cloud Required

Imagine this: after every customer meeting, structured Jira issues are created automatically. You just paste your notes into an AI, and it does the rest. Atlassian already offers that kind of magic in Jira Cloud: natural-language search, automatic summaries, and issue creation from unstructured text. But not everyone wants to move to the cloud, and many teams plan to keep using Jira Data Center through 2029. In this article, we show how to get many of the same benefits on-premises with Jira Server and your own AI stack: GDPR-compliant, resilient to Cloud Act exposure, and without data leaving your environment.

Where AI Helps (and Hurts) Across Different Coding Scenarios

My takeaways from Stanford University’s Developer Productivity Study

Spec-Driven Architecture: When Agents Build, Architecture Must Speak

Spec-Driven Development gives agents a clear foundation for implementation. What it doesn’t solve is how a portfolio of systems stays coherent. Spec-Driven Architecture applies the same principle at the architecture level, using contracts as versioned boundaries and guarantees—enforceable in agentic workflows and in the CI/CD pipeline.

REST Beats MCP

Instead of using dedicated APIs, agents can operate existing web applications directly. Like humans, they rely on the most consistent implementation of hypermedia and benefit from existing context, validation, and access controls. Using an expense report as an example, I show how agents can automate complex, context-dependent tasks—without having to implement new APIs.

Spec-Driven Development is Domain-Driven Design’s Impatient Cousin

Why BMAD won’t save you

The right size of a Data Product

Setting the boundaries of data products incorrectly can lead to integration issues, unclear ownership and duplicated logic. This guide offers practical heuristics for creating data products of the right size.