Systems | Development | Analytics | API | Testing

Here at Speedscale, our monorepo has 19 Go projects in CI. Fifteen produce container images for both amd64 and arm64; a few of those need CGO. We also publish cross-compiled CLI binaries for macOS, Linux, and Windows.

Every QA team carries a list of things it hasn’t gotten to yet, and that list tends to grow at about the same rate as the application portfolio. There are more tests to automate than there is time to write them, coverage that stops short of the applications nobody wants to touch, and workflows that have stayed manual long enough to become part of how the team works. Automation chips away at that list without ever quite emptying it, because the constraint is rarely how fast the existing tests run.

If you only read the headlines this year, you’d think AI makes shipping good software easier than ever. Yet, this was the year of very public AI-coding incidents. PocketOS’ production database deletion and a Vercel AI agent shipping unverified code are just two examples of AI-authored code shipped with confidence that turned out to be wrong. And, of course, these AI-coding incidents are distinct from Agentic AI orchestration incidents like the HuggingFace hack by OpenAI.

Standing up a production agent means taking it through six stages: At API & AI Summit, we announced the evolution of Kong Konnect into the AI Connectivity Platform and announced a slate of launches built around that idea. Here's what we launched, where each piece fits, and what you can use today. An agent that only runs when a person prompts it isn't autonomous. Real autonomy starts when something happens in the world, like a payment failing or a ticket opening, and the agent responds.

Traditional observability was built around requests. A request comes in. A service processes it. A response goes out. You trace what happened in between. That model works well for APIs. It breaks down for AI. With agents, a single user request can trigger multiple model calls. An agent might invoke a tool, call another agent, query an MCP server, retry a model, hit a guardrail, and call another tool before finally producing a response. And all of this happens non-determinstically.

At SmartBear, we’re dedicated to empowering teams by delivering application integrity: continuous, measurable assurance that their software works as intended, even as AI accelerates the SDLC. The AI Test Innovators Award: QMetry + Reflect celebrates customers who are using AI to reimagine how they create, maintain, and manage tests – without sacrificing rigor or reliability.