Systems | Development | Analytics | API | Testing

Steering lets a client redirect an agent while the agent is still working, so a follow-up message reshapes the answer in flight instead of cancelling the Run and starting over, or waiting for it to finish. In the AI Transport SDK, a Run encapsulates the agent's output for a single turn (including multiple iterations around an agentic loop). The last release, v0.5.0, made a single agent turn survive a crash by splitting a Run into re-attemptable Steps.

You opened the AI assistant for the first time with a fair amount of hope. You typed "write test cases for the login page." You got eight test cases back in about three seconds. Valid login. Invalid password. Empty username. Empty password. The kind of list you could have written in your sleep, missing every scenario that actually matters for your product. So you closed the tool and thought: this is fine for the basics, but it does not really get testing. AI is overhated.

If you've been following the agentic AI wave, you've probably noticed that the conversation tends to center on the agents themselves: which LLM to use, which orchestration framework to pick, which use cases to tackle first. But a growing body of analyst research is pointing to a different bottleneck, one that's hiding in plain sight: integration. Forrester's David Mooter argues that integration must sit at the center of your AI strategy — not as plumbing, but as a strategic capability.

In the first part of this series, I argued that discernment, the ability to recognise when an AI-generated answer is wrong, is becoming one of the most valuable capabilities inside an organisation. The question this piece addresses is simpler and harder: who is actually being given the opportunity to develop it? The AI opportunity gap is real. It is not primarily a gap in access to tools. It is a gap in permission. And I believe that gap starts earlier than most leaders realise, often in school.

Most AI content assumes your data is in the cloud. But for a meaningful segment of enterprises, cloud-only AI tools block them at the pass. For regulated industries like manufacturing and healthcare, data residency requirements, compliance mandates, security policies, and simple operational reality mean sensitive data must remain on-premises.

With 34% of software failing to meet quality standards, and 77% of AI failures significant enough to damage user trust and product integrity, new research highlights why global brands must pair AI orchestration with expert human insight.

AI is changing how software gets built. That raises the bar for every team responsible for keeping it working. SmartBear has been developing AI capabilities across the entire quality lifecycle that help teams deliver software they trust will work at AI speed and scale. Today, SmartBear announced it has achieved AWS AI Software Competency status in the Agentic AI category through the AWS Partner program.