Focus

Agentic Software Engineering

AI is changing the way we build software. Agentic Software Engineering takes it further. Instead of applying AI to isolated steps, specialized agents handle distinct parts of the software lifecycle, such as planning, implementation, testing or documentation. These agents follow defined roles and are coordinated within structured workflows. Engineers set goals and constraints, manage agent interactions and review results. This creates a new kind of collaboration between humans and machines, shifting the focus toward quality, speed and scalability.

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What to Do While the AI Is Thinking

AI can write code while we wait — but that doesn’t mean we suddenly have free time. Whether we should think, multitask, review, or rest depends on what kind of work we are doing.

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AI and Elaboration: Which Coding Patterns Build Understanding?

AI tools let you complete coding tasks without connecting new information to your existing mental models—a cognitive process known as elaboration that is crucial for building understanding. But some AI interaction patterns preserve this elaboration while others bypass it entirely. Let’s explore what elaboration is, why it helps with learning, and how we can use AI tools in a way that helps with this process rather than circumventing it.

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Fetch-Tools vs. Browser-Rendering in Agenten-Setups

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I sandboxed my coding agents. You should too.

LLM coding agents are extremely powerful because they can run programs on our computers using our permissions. However, this same power also makes us very vulnerable. It only takes one mistake or one prompt injection to compromise the whole system.

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Speed vs. Skill

Every AI coding tool promises the same thing: unprecedented speed, effortless productivity, freedom from tedious work. The pitch is compelling. But what if feeling more productive and being more capable aren’t the same thing? Research shows that automation makes us feel more productive while eroding our skills. Let’s examine this tension through the lens of cognitive psychology.

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Context Engineering: Managing AI-Generated Code Complexity

Keeping your scope small was always a good idea

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Swiss Army Knife for Salesforce: LLM with In-Memory Database

Large Language Models (LLMs) struggle with transforming large datasets, particularly when performing aggregations that require calculations - essentially anything where you’d normally use GROUP BY or ORDER BY in SQL. But what if we need to retrieve extensive data from a system and process it with an LLM, but only have an API with limited functionality available? In this blog post, I present a solution I’ve implemented and tested.

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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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Das Versprechen der Agenten

Mit agentischen KI-Systemen wird uns eine völlig neue Welt versprochen. Was ist eigentlich die Natur dieser Systeme und was bedeutet das für die Art und Weise, wie wir mit ihnen umgehen werden? Wir gehen der Frage nach, ob wir das alles geschenkt bekommen, oder ob wir uns dafür noch anstrengen müssen.

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Agenten - Kreise - Firmen

In Teil 2 habe ich Ihnen versprochen, die probabilistische Weltsicht zu beleuchten, mit der Machine Learning Experten schon immer vertraut sind, und die uns allen mit agentischen KI-Systemen bevorsteht. Wir befassen uns damit, was genau in unserem Fall das Lernende System ist, welche neue Rolle dabei Menschen einnehmen, und wie die Vision des CEO von Microsoft Satya Nadella zur Firma der Zukunft aussieht.

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