Systems | Development | Analytics | API | Testing

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.

Most mobile automation programs don't stall because a tool can't tap a button. They stall on everything around the tap: the setup before the first test, a device lab nobody can keep current, maintenance that grows faster than coverage, and the moment someone asks "are we ready to ship?" and nobody has a clean answer.

Imagine hiring the sharpest analyst you have ever met. They know the industry cold. They reason like nobody else on the team. They write like a Nobel laureate. Then you realize you never gave them a badge, a laptop, or a login to the CRM. And a week later, nothing has shipped. Every task still routes back through you, because the analyst can think but can’t touch anything. You’ve just hired a very expensive conversation partner. That is most people's experience with AI agents today.

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.

Generating code has never been cheaper. Validating it is where teams are losing the most time, and every handoff between tools makes it worse.

Two questions stall almost every agentic AI rollout: Is it secure? And what is this going to cost us? Both get harder to answer the longer you wait to ask them, and easier to answer than most teams assume. If you've already invested in a robust data analytics platform like Qlik, you're closer to a secure, governed, and cost-effective agentic AI deployment than the market's “rip and replace” narrative suggests.

Most enterprise AI infrastructure conversations land on the same tension. Builders want self-service: instant access to GPUs, a stable endpoint for a model, a notebook, or a fine-tuning run that starts without a ticket. Platform teams want per-team quotas, identity-aware access, audit trails, tenant isolation, and cost attribution they can defend. Both sides are right, and most platforms make you choose between them because self-service and control get layered onto a base that was designed for neither.