Systems | Development | Analytics | API | Testing

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.

Unlock AI-powered insights in Salesforce by activating warehouse data with AI Columns in Fivetran Activations.

AI test case generation tools are transforming how QA teams create, maintain, and execute tests by automating repetitive work and improving coverage. Teams that adopt AI for QA now will reduce manual test creation time while expanding their test coverage. Software testing has always been a balancing act between thoroughness and speed. You want comprehensive coverage, but you also want to ship features before your competitors do.

The recently released OWASP, “A Practical Guide for Securely Using Third-Party MCP Servers,” highlights a fundamental challenge in modern AI deployments: how do we govern, secure, and audit systems that are inherently non-deterministic? Unlike traditional, static software, AI agents dynamically adapt their execution paths, tool selection, and decisions based on context and real-time resources, allowing the same agent to achieve identical goals through entirely different approaches.

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.

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.