AI has a visibility problem, but not in the way most people think. The models get the spotlight, the keynote demos, the investor excitement, and the polished slide decks. Meanwhile, the work that decides whether AI actually works in production often happens somewhere less glamorous, inside pipelines, data contracts, feature definitions, schema changes, observability dashboards, […]
The issue is not that agents are overhyped. The issue is that people are forgetting what they already knew about distributed systems. Over the past two decades, we collectively agreed on a few basic principles in software architecture. Modular systems make change easier, loose coupling keeps components from holding each other hostage, and clear contracts
Agent Systems Are Still Distributed Systems Weiterlesen »
Someone came up to me at a conference and asked how they should implement the Saga pattern in Apache Flink and I think this is worth a written blogpost as well. Their team came from a classic microservices architecture, where Sagas had become the standard answer whenever one business transaction crossed several services. Now they
Saga Pattern in Apache Flink: When It Works, When It Breaks, and What to Do Instead Weiterlesen »
A real-world experiment with Claude Code, Jira, GitLab and OpenTelemetry to measure agent costs, reduce token waste and find out whether AI agents are actually cheaper than human developers. Why do we celebrate the empty gas tank? My tax advisor asked me a fairly reasonable question: “Could I use agents to automate some of the
Building an AI Development Agent: From Jira Ticket to GitLab Merge Request Weiterlesen »
The original plan was never to do a world tour. It just slowly escalated from „one conference and a few customer meetings“ into Dubai, Portugal, Germany, Singapore, Japan, Hawaii, Los Angeles, London, Switzerland, and back to Dubai with enough flight segments to emotionally qualify as cabin crew. Looking back, the most interesting part was not
90 Days Around the World: AI, Business Travel, and What Companies Still Get Wrong Weiterlesen »
Irregular operations expose weak aviation data flows. This article explains why weather, strikes, delays, diversions, and network knock-on effects require real-time data for better recovery, coordination, and passenger communication.
Irregular Operations in Aviation: Why Real-Time Data Matters During Disruption Weiterlesen »
Airport operations depend on timely data across ground handling, turnaround, baggage, gates, and passenger flow. This article explains why late data creates physical friction, delays, and passenger confusion.
Airport Operations: Why Real-Time Data Matters for Ground Handling, Baggage and Gates Weiterlesen »
Predictive maintenance in aviation depends on real-time aircraft condition data, operational context, parts availability, and engineering discipline. This article explains why late data turns aircraft health insights into operational risk.
Aircraft Maintenance and Health: Why Real-Time Data Matters for Predictive Maintenance Weiterlesen »
Air cargo depends on current shipment, capacity, aircraft, customs, and handling information. This article explains why late data creates friction, cost, and operational risk in cargo operations.
Cargo Operations: Why Late Data Turns Air Freight Into Guesswork Weiterlesen »
Passenger disruption management depends on real-time operational data. This article explains why canceled flights, missed connections, rebooking, and AI chatbots expose stale data as an operational risk.
Passenger Disruption Management: When Late Data Gives Passengers the Wrong Answer Weiterlesen »