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.

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.

The digital front door is moving beyond portals, online forms, and basic appointment booking. In 2026, AI is making patient access more responsive by helping patients find the right provider, schedule care, verify coverage, complete intake, and prepare for visits through connected digital workflows. The shift comes as healthcare organizations move AI from experimentation into real operations.