Systems | Development | Analytics | API | Testing

You can add AI-driven testing to your Jira workflow by handing tickets to the QA Lead Agent in SmartBear BearQ for Jira. Move a finished ticket, and the agent checks for an existing test, links or creates one, runs it, and reports back. If your team builds with coding agents like Claude or Cursor in Jira, BearQ, the agentic QA system, adds the quality step to that AI-driven software development lifecycle (SDLC).

10x faster coding. Same QA. Until now. You're coding faster with AI. But if your agents are siloed and disconnected, those gains aren’t yet showing up in your quality or delivery – it's getting lost in the handoffs. Join SmartBear for a live demonstration of the connected agentic SDLC. See how BearQ extends beyond creating tests to coordinating quality work across Jira, GitHub, and AI-powered workflows – detecting issues, triggering the right next action, and carrying context from one agent to the next.

How do you solve problems that nobody reports? In this episode, Robin-Yann Storm, tool design director and former developer at IO Interactive, Guerrilla Games, and Apple, shares how a small usability issue with a 3D transform gizmo led to a workflow improvement that developers still use years later, saving thousands of hours. The discussion looks beyond the solution itself to examine the process of discovering the problem, understanding its root cause, and exploring how to solve it. Robin-Yann explains why observing users is often more valuable than collecting feature requests and how small UX improvements can deliver outsized returns.

AI may make coding faster, but can your safety, security, and verification practices keep pace? In this webinar, Perforce Director of Product Steve Howard examines how the emerging AI Development Lifecycle (AIDLC) could reshape embedded software development and specifically V&V processes. You'll also learn where AI can help, where its limitations remain, and what organizations should consider as they prepare for AI-driven development.

Going from a written requirement to an executable test usually means writing the steps and data yourself, then mapping each one to the application by hand. Pair Tricentis AI Workspace's requirement analysis with Tosca's agentic automation to go from a Jira ticket to a running, reusable test case for your SAP Fiori applications. In this walkthrough, you will see how to generate a test case from a requirement, map it to the Fiori UI, and save it for regression.

Agents are easy to build and hard to measure. When your company runs hundreds or thousands of them, hand-written evals can’t keep up, and it’s unclear which agents are correct, efficient, and ready for users. In this conversation, Ryan Yackel, SVP of Product Marketing, and Andrew Demers, Product Manager for Agent Score at Tricentis Labs, explain how to measure agent quality, score agents automatically, and trace issues back to behavior.