Systems | Development | Analytics | API | Testing

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.

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.

Today, we're excited to announce Kong Mesh 2.13. Kong Mesh 2.13 delivers full support for Mesh Identity for Kubernetes and Universal mode. Plus, it's been designated as a Long Term Support release, with support for a total of 2 years. But first, what's Kong Mesh for the uninitiated?

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.

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.

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.

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.

This is the fourth and final installment in our series about bringing AI to Bitrise. In Part 1, we explained why we built our own AI coding agent. Part 2 covered our browser-integrated AI Assistant. Part 3 detailed how we brought AI to the Bitrise Build Cloud. In this final post, we'll explore how we unified these efforts into a cohesive AI Platform.

Software engineering leaders, platform leaders, and AI/ML leaders responsible for release velocity, quality, and risk management.

If you’re an SAP customer, you’ve probably heard the same pitch more than once: just wait a little longer. Your migration to S/4HANA will unlock the agility you need. Your SAP Business Technology Platform (BTP) investments will catch up soon. Your workflows will get faster. Your data will become more accessible. Just a little more time. But what if you can’t wait? What if your supply chain teams are buried under manual work?