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Artificial Intelligence

Articles, podcasts, talks, and more about Artificial Intelligence.
Blog Post

The Shifted Complexity: From Waterfall through Agile to AI

Software projects have always been complex, but that complexity has kept shifting: from project management to the team, from the team to the individual developers. AI is accelerating this shift dramatically. Looking through the Cynefin lens reveals a pattern that keeps repeating itself and is currently escalating. The consequence: a massive increase in mental load for developers and the question of how we want to deal with this going forward.

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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.

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The Data Dilemma, Part 2

A Minimum Viable Data Mesh in four layers

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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.

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Beyond Claude & GPT

Sure, we enjoy trying out the latest frontier model as much as anyone. But in day-to-day project work, what matters most is control over cost, data, and availability. That’s why we increasingly rely on open-weights models paired with open source tooling. Here’s what our setup looks like.

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Trust but Sandbox

Running Coding Agents Safely with Docker Sandboxes

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RAG and Fine-Tuning

Large Language Models (LLMs) don’t always produce reliable results. In practice, one thing becomes clear: combining Retrieval Augmented Generation (RAG) and fine-tuning can make them more robust. This post explains how the two approaches complement each other – and why a production feedback loop is critical.

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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.

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The Right Kind of Hard

On dead weight, false clarity, and other hidden costs of spec-driven development

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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.

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Where AI Helps (and Hurts) Across Different Coding Scenarios

Where does LLM-assisted software development improve developer productivity—and where does it fall short? Instead of treating AI in software development as a one-dimensional productivity booster, we explore the question across several dimensions suggested by a Stanford-adjacent study: project maturity, task complexity, and the popularity of the programming language. The goal is to create a more realistic expectation baseline for both software developers and leaders—well away from today’s hype.

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REST Beats MCP

How AI agents actually benefit from hypermedia

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

Spec-driven development tools like BMAD promise to fix the requirements bottleneck that agentic development teams face. But is it actually a good fit for your organisation?

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Agents good in the end?

Software development consists of a constant chain of trade-offs. As long as I’ve been building systems, there have always been things you don’t do despite wanting to do them. But now, with agents, we can fulfill all our dreams and finally build everything we always wanted and had the feeling we were missing. Spoiler alert: we shouldn’t do that.

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Understanding AI Coding Patterns Through Cognitive Load Theory

Developing with AI through the cognitive Lens

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Neuland reloaded

When Angela Merkel said in 2013 that the internet is ‘Neuland’ (uncharted territory) for all of us, the amusement online was considerable. Viewed from some temporal and substantive distance, one can now recognize a perspective from which she was indeed correct.

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I sandboxed my coding agents. Now I control their network.

I want my AI coding agents to work independently, but I don’t want them to have unrestricted access to the internet. In this post, I describe how I routed all network traffic from my development sandbox through a strict proxy allowlist, allowing only a small set of explicitly approved domains. This setup finally gave me enough confidence to loosen the guardrails without constantly staying in the loop.

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From Vibe Coder to Code Owner
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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.

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Let’s Not Normalize Insecure AI Assistants

AI assistants like OpenClaw promise convenience, autonomy, and increasingly personalized help. But beneath that promise lies an architecture that quietly combines private data, internet access, and exposure to untrusted content—the lethal trifecta of security risk. Sandboxing and physical isolation help, but they don’t address the core problem. As we add more capabilities, the potential blast radius only grows. This post is a case for slowing down, questioning defaults, and refusing to normalize insecure architectures.

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I sandboxed my coding agents. You should too.

My Lima VM + JetBrains Gateway setup for safe agentic development

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From FOMO to Focus

AI is on every agenda – but where do you start? Many companies launch multiple AI initiatives in parallel, driven by the fear of missing out. The result: scattered resources, lack of prioritization, and unclear business impact. AI Opportunity Mapping provides a solution: it systematically guides you from vague AI visions to concrete, prioritized use cases with real business value. In five steps, teams develop AI opportunities in a structured way and make well-founded decisions. The result: focus instead of FOMO, clarity instead of chaos.

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Modern Legacy (thanks to AI)

The discussion around software modernization is currently dominated by solutions involving artificial intelligence (AI) in software development. AI-assisted code completion, agentic software engineering, and other tools promise to massively accelerate development processes. The obvious thought is to apply these tools directly to existing code as well: refactorings, re-documentation, or even generated code for new features in legacy systems. Technically, much of this is already possible, and the buzzword industry is once again in full swing. Yet these code-centric, AI-driven approaches can be shortsighted for organizations tangled in a jungle of legacy systems. A modernization that focuses solely on code quickly leads to modern legacy: systems that appear technically advanced and well documented thanks to AI, but whose actual purpose has long become questionable.

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Think Locally: On-Premise LLMs as Drivers of Competitive Advantage
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Software Analytics going crAIzy!

I’ve long been an advocate of treating software like data and analyzing it accordingly with tools like Python, pandas, Jupyter Notebook, Neo4j, and jQAssistant. In the era of AI and code transformation tools, seeing software as data gets more and more important when you want to improve your software system at a large scale. This blog post is a short interim report from me about my efforts taking software analysis to the next level.

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The Sovereignty Trap: Between Tiananmen and Trump

We talk a big game about values and privacy, yet depend on AI that either denies historical atrocities or could cut us off tomorrow. Is this what Europe gets for dropping the ball on high tech infrastructure?

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Swiss Army Knife for Salesforce: LLM with In-Memory Database
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From Data Graveyards to Knowledge Landscapes

Europe is sitting on a wealth of public data—but much of its potential remains untapped. The challenges are well known: fragmented portals, incompatible interfaces, and growing reliance on non-European platforms that slow innovation. While new industrial data spaces are emerging—enabling secure and sovereign exchange of sensitive information—public and industrial data ecosystems remain largely siloed. This article explores how Artificial Intelligence (AI) and the Model Context Protocol (MCP) can help bridge that gap and accelerate Europe’s shift from Open Data to Open Knowledge—supporting digital sovereignty and delivering greater value to society.

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AI — Behind the Buzzword Garbage

Tired of AI hype? Me too. But beneath the buzzwords lies real value for developers. Tools like Claude Code save me hours on routine tasks, freeing me to focus on what matters: understanding problems and building the right solutions. It’s not magic—it’s practical support that makes development faster without replacing our core skills.

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First Agile, Then Agentic

Why AI Won’t Help You

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Context Engineering

Have you ever wondered how chatbots, copilots, or virtual assistants seem to know just the right things to say — or sometimes, spectacularly fail? The difference often comes down to one crucial skill: context engineering.

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Beyond the hype: An engineer’s journey into ReBAC and AI with the Model Context Protocol

In this article, I share my experiences on my journey into the AI world. During this journey, we’ll build our own Model Context Protocol (MCP) Server using C Sharp, learn about access management with relationship based access control (ReBAC) on the way, and in the end I’ll provide my thoughts on the current state of AI and MCP, focusing on security and UX.

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Building Standardized AI Tools with the Model Context Protocol (MCP)
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Transcribing podcasts with large language models

Automatically transcribing podcasts with multimodal AI sounds like a no-brainer, but the devil is in the details.

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RAG: The Architecture of Reliable AI

How can we ensure that AI systems are precise, transparent, and always up to date? All Large Language Models (LLMs) have a cut-off date where their world knowledge ends. And they know nothing about your company’s internal information. Even the leading models still have hallucination rates we can’t completely ignore. Yet they offer enormous potential for productivity, efficiency, and creation. Retrieval-Augmented Generation (RAG) addresses exactly this issue: LLMs are enhanced through targeted information retrieval.

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Document Ingestion

The Foundation of a RAG System

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Retrieval-Augmented Generation

How do we handle situations where an LLM-driven system requires highly specialized enterprise knowledge that wasn’t included in the original model training? While LLMs have enormous potential to answer generic queries based on their comprehensive knowledge from training, they show limitations when it comes to current, specialized, or verified information.

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When the Worldview is Shifting

Large Language Models (LLMs) can feel like magic. We observe them and imagine that they work in a certain way. Then something surprises us, shattering the illusion and fundamentally reshaping our understanding. In this blogpost I’ll show you one such surprise from my own experience, and I believe that your worldview will have changed at the end.

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Better RAG With Hybrid Search

Why the overemphasis on vector search does not solve the problem

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LLM-assisted Abbreviation Mining for Legacy Systems

This blog post shows the process of mining abbreviations and discovering first concepts a COBOL legacy mainframe codebase is made of with the help of Large Language Models. It uses Python, pandas and Claude 3.5 Sonnet to generate insights that can be gathered from such a simple thing like a list of files.

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Generative AI: The End of “Too Expensive” in Business Software?

”We’ve found no use cases.” — No, you’ve got too many. Let’s take a look at how Generative AI will change business software beyond chatbots. What features might become possible that weren’t before, or were just too expensive?

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Here’s All You Need To Know To Start Building With Generative AI
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How To Build a Data Product with Databricks

In today’s data engineering, the focus is primarily on developing modular data products. This article outlines the advantages of modularity over monolithic data pipelines and explains, step-by-step, how to develop data products using Databricks – from defining a data contract to creating and implementing Databricks Asset Bundles, setting up a CI/CD pipeline, and publishing metadata.

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A natural language calculator

In my prior post I’ve written about how to run a chat with a large-language-model on your PC. This time I want to focus on scripting this with Node.js and letting the AI- and the “normal”-world interact with each other.

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AI Tools in Business Environments

and which also make your everyday life easier

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Running an AI Chatbot on Your Own PC

Llama.cpp, gpt4all and others make it very easy to try out large language models. Here’s a short guide to trying them out under Linux or macOS.

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How to use Apple Shortcuts to integrate GPT-4o in macOS and iOS

Apple Shortcuts is a powerful app that lets you create custom workflows with multiple steps using your apps and content. You can also use it to interact with web services and APIs, such as OpenAI’s Chat Completions API for GPT-4o, which can generate text completions for any prompt or task. Yup, the thing that’s behind ChatGPT.

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How AI will replace my job

I started using ChatGPT for my work. Here is what it taught me and what AI might hold for the future of software development and consulting

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Fairness and Artificial Intelligence

Classical software testing cannot simply be transferred to AI. Model governance and internal audits are required to ensure fairness.

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Ethics and Artificial Intelligence

Artificial intelligence is forcing its way into many fields of application. Now it is important that it works in a responsible, secure, and transparent way. The regulation of AI systems is a legal, societal, and technical topic that demands broad awareness and that will become increasingly important in the years to come.

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MLOps and Model Governance
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What tracks do we leave behind with technology?

Bei INNOQ setzen wir uns immer mehr mit KI und Machine Learning auseinander, allerdings mit ihrem sinnvollen Einsatz bei unseren Kunden und Projekten. Beste Voraussetzungen also für einen spannenden Diskurs.

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Handling German Text with torchtext

There is a growing list of tools that are ready to be used with non-English texts. We show common ways to integrate them in torchtext and use their language-specific options.

Case Study

Data Governance without handbrakes: How AI accelerates time-to-value in Data Mesh

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Case Study

Answers instead of search results:
Sprengnetter unlocks real estate expertise with Generative AI

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News

INNOQ launches Data and AI Consulting Services
Case Study

SACAC optimizes the quotation process with a customized software solution

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Case Study

Gaining a competitive edge in the quotation process through Machine Learning

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Training

Designing software architectures for AI and ML systems

iSAQB® Training
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