Systems | Development | Analytics | API | Testing

When you're building agentic AI applications with multiple agents working together, the infrastructure challenges show up fast. Agents need to coordinate, users need visibility into what's happening, and the whole system needs to stay responsive even as tasks branch out across specialised workers. We built a multi-agent travel planning system to understand these problems better. What we learned applies well beyond holiday booking.

Without a fuel gauge in your car, you'd have to rely on gut feeling to know when to fill up, and that's risky. You might end up stranded on an empty road without gas. The same principle applies to software we use every day. Embedding analytics (charts, graphs, reports and dashboards) into your app means your users can base their decisions on fast, powerful visualizations of real-time data.

The digital economy has upgraded from simple transactional interactions with users. Now consumers demand the Autonomous Digital Experience (ADE) – the customer journey is driven by predictive, self-learning systems, which is essential for competitive success. This is driven by Predictive Personalisation, which uses machine learning to predict personalised affinity and intent of user actions, delivering personalised content, products and messages in real-time.

See how easily Konnect Metering & Billing transforms API and AI traffic management into new revenue streams. We've talked about why 2026 is the year of AI unit economics. There, we explored the "2025 hangover" where organizations realized that without financial governance, AI isn't just a science project but has become a margin-bleeding cost center. But "governance" and "monetization" shouldn't just be buzzwords in a resolution; they need to be part of your active infrastructure.

Software development has evolved to a point where updates ship more frequently than ever – sometimes multiple times a week. But rapid releases demand equally fast validation. Traditional full regression cycles take too long and can block delivery, especially when only a small feature or module has changed. Delta testing addresses this challenge by testing just the updated areas of the product. It allows teams to maintain quality while delivering incremental improvements quickly.

The next generation of debugging doesn’t depend exclusively on the quality of AI models, but it’s heavily dependent on feeding AI tools the context they need to be useful. AI coding assistants have transformed how we write code. For example, GitHub Copilot, Cursor, and ChatGPT can generate Stripe integration boilerplate in seconds. They'll scaffold your payment flow, suggest error handling patterns, and even write unit tests.

Last Tuesday, we kicked off our first AI developer meetup of 2026 with a packed room and over 350 signups! This was our first content-focused event since organizing AI Engineer Paris 2025, and it was a great night bringing the AI dev community together to share ideas and learn from some of the most exciting builders in the space. Want to join next time? Follow our global events calendar to stay in the loop. Our meetup's theme was coding agents. We heard from speakers at Koyeb, OpenAI, and LangChain.

AI now sits inside most software, whether teams planned for it or not. A recent report from Stanford HAI found that AI adoption jumped from 55 percent in 2023 to 78 percent in 2024.

If you’ve recently upgraded to Debian 13 (“Trixie”) or a newer version of Ubuntu and suddenly started seeing security warnings when running apt update (or apt update --audit), don’t worry. You didn’t do anything wrong. This is a side effect of a broader security change across modern Linux distributions. SHA-1 signatures are being deprecated, and repositories that still rely on them may now trigger warnings or audits.

Modern teams need to move sensitive data from on-prem SQL Server into Salesforce safely and predictably. This guide explains how to design, implement, and operate a secure ETL that balances performance with controls. It is written for data engineers, platform owners, and security leads who support regulated workflows. You will learn core components, common pitfalls, architecture patterns, and a phased implementation plan with code examples.