Systems | Development | Analytics | API | Testing

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

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.

The CMO Blueprint: Writer's Diego Lomanto On Navigating AI's Investment Frenzy

Diego Lomanto, Chief Marketing Officer at Writer, joins Snowflake CMO Denise Persson to discuss the current investment frenzy in AI and how to navigate the hype. Learn how understanding the technology cycle can help marketers identify real value and drive wealth creation. Discover Diego's unique career path from developer to CMO and how his background in technology and engineering shapes his approach to marketing. Gain insights into the societal impact of technology and the importance of reshaping and reforming to drive a better standard of living for humanity.

Stop Picking Sides in Enterprise AI

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.

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

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.

Snowflake Cortex AI for Cybersecurity: How Sophos Built Real-Time Threat Monitoring

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.

Introducing Advanced AI Observability: See the Whole Agent Journey

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.

Why Your AI Strategy Is Failing

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.

Human Judgment Is the Missing Variable in Your AI Strategy

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.

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

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.