Systems | Development | Analytics | API | Testing

How to Automatically Fail Over Between LLM Providers | Kong AI Gateway

Learn how to automatically switch between LLM providers when your primary provider becomes unavailable. In this AI Gateway Discovery video, we configure automatic failover using Kong AI Gateway’s Priority load balancing algorithm. You’ll learn how to assign models to priority groups and fall back to a backup group when all targets in the preferred group are unavailable. Follow along to set up automatic LLM failover and make your AI applications more resilient to provider outages.

Best AI Video Tools for Software Demo and Team Marketing Workflows

Sponsored by Magic Hour. This article was prepared with AI assistance. It compares documented product capabilities and practical workflow considerations; it does not report hands-on tests, measured performance, or a universal ranking. Choosing an AI video tool is partly a creative decision and partly a systems decision. A clip must look appropriate, but a marketing team also needs to know where its inputs came from, how the result will be reviewed, and whether another colleague can reproduce the process. An impressive demo answers only one of those questions.

How Cloudera AI Agents Automate Retail Product Onboarding

Retail merchandising teams spend countess manual hours evaluating new product submissions—crunching revenue models, analyzing sales cannibalization, and writing supplier rejection emails. Powered by *Cloudera AI agents* , this video demonstrates how retailers can transform that tedious, bottlenecked onboarding workflow into a seamless, automated process. As soon as a new product is submitted, autonomous background agents map it against existing inventory, evaluate growth risks, assess market saturation, and highlight specific low-performing SKUs to replace.

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.

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.

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.

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.

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.

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.