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bugraptors

When AI Tests AI: Breaking the Recursive Trust Loop in Enterprise QA

Oct 1, 2026 By Prateek Goel In BugRaptors
Enterprise adoption of autonomous agentic workflows has shifted software quality engineering. Deterministic automated testing remains foundational for API behavior, schema validation, authorization, and data integrity. Dynamic, non-deterministic model outputs, however, require additional layers of verification. To manage scale, modern AI application testing increasingly relies on AI test AI workflows using LLM-as-a-judge setups.
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Catch bugs and UI issues automatically with QA agents | SmartBear BearQ

Oct 1, 2026 By SmartBear In SmartBear
In this video, you’ll see how SmartBear BearQ's QA agents catch bugs, UI and UX issues, and accessibility gaps automatically as they explore or test your applications, zero test script writing necessary. Whenever the Explorer Agent investigates your app or the Tester Agent runs a test, the QA Lead Agent is watching in the background for anything that looks wrong. Each suspected issue arrives with a clear title, a description, expected versus actual behavior, steps to reproduce, and a link to the evidence that shows exactly when it was found.
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qlik

Stop Picking Sides in Enterprise AI

Oct 1, 2026 By Brendan Grady In Qlik
One message coming out of Dreamforce caught my attention: context is moving to the center of the enterprise AI conversation. Good. We’ve been arguing for a while that context is what turns AI from an impressive interface into something genuinely useful for the enterprise. Salesforce’s AIforce approach is about bringing its data, workflows, business logic, semantics, permissions, security, and governance into the places people increasingly want to work, including Claude and Slack.
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smartbear

Confidence in AI-generated code is rising in lockstep with its failure rate

Sep 30, 2026 By Fitz Nowlan In SmartBear
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.
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appian

Human Judgment Is the Missing Variable in Your AI Strategy

Sep 30, 2026 By Jackie Jubien In Appian
Across teams, organizations, and industries, people are starting with AI instead of the problem they want to solve. As a result, AI outputs from different tools: This is a process failure, and it's accelerating. Human-in-the-loop (HITL) AI is a framework that integrates human oversight directly into the machine learning lifecycle. Rather than relying on fully autonomous systems, HITL uses humans and machines collaboratively to train models, evaluate outputs, and handle complex decision-making.
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Why Your AI Strategy Is Failing

Sep 30, 2026 By ThoughtSpot In ThoughtSpot
Every leadership team is arguing about which AI model is best, but that debate is a distraction from the real problem. In this episode, Jeff McMillan, founder of McMillanAI and former head of firmwide AI at Morgan Stanley, breaks down the real bottlenecks behind enterprise AI strategy. He shares why fixing your data matters more than picking a model, why your best AI leader is probably already inside your company, and why AI eliminates tasks rather than entire jobs.
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kong

Introducing Advanced AI Observability: See the Whole Agent Journey

Sep 30, 2026 By Alex Drag In Kong
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.
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Snowflake Cortex AI for Cybersecurity: How Sophos Built Real-Time Threat Monitoring

Sep 30, 2026 By Snowflake Inc. In Snowflake
Looking to scale your cybersecurity and threat monitoring using generative AI? Learn how Sophos leverages Snowflake Cortex AI to analyze massive security telemetry datasets. Jason Mullin, Director of Enterprise Data and Analytics at Sophos, shares how his team modernised their security analytics pipeline on the Snowflake Data Cloud. By integrating Cortex AI alongside tools like CoWork and CoCo, Sophos cut security analysis times and enabled AI-driven insights across their organization.
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katalon

AI mobile testing: where AI actually removes work from mobile app test automation

Sep 30, 2026 By Alex Martins In Katalon
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.
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thoughtspot

Why Your AI Agent Is Only As Good As the Tools It Can Reach

Sep 30, 2026 By Sam Bowerman In ThoughtSpot
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.
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