Systems | Development | Analytics | API | Testing

AI agents are moving rapidly from experimentation to execution, and Snowflake CoWork is making it easier for teams to put AI to work across everyday business workflows. But knowing how to use CoWork effectively is only the first step. The bigger question is: how do you make sure the data powering those workflows is complete, current, and reliable enough to trust?

Choosing between Docker Compose and Kubernetes can feel overwhelming, especially when both play an important role in modern containerization strategies. In this video, Zend Professional Services Manager Adam Culp breaks down Kubernetes vs Docker Compose. You’ll learn the essentials of container orchestration, discover when simplicity is an advantage, and understand when it's time to scale—plus, he recommends a new tool that can help, whether you choose Kubernetes or Docker Compose.

In this video, you'll learn how SmartBear Swagger Agents, built right into SmartBear Swagger Studio, cut the process of manual API governance reviews and documentation from weeks to minutes, generating API definitions that are specific to your APIs and compliant with your organization's governance rules from the start.

Release notes are the thing that always gets written last, usually by whoever has the least context, usually the morning after ship day. Everything you need is already sitting in GitHub, it just isn't in a form anyone outside engineering can read. In this video, we use AgentSpot to build a Release Notes Workflow that reads what's been merged in GitHub, translates the changes into human-readable notes, posts them to your team's Slack channel, and keeps a running Slack canvas so every release stays in one place.

Writing tickets is the tax every PM pays. You know exactly what needs to get built, then you spend an hour turning it into properly scoped Jira issues with acceptance criteria, labels, and the right epic. AgentSpot does the writing for you. In this video, we use AgentSpot to build a Product Assistant that turns a rough feature idea into fully drafted Jira tickets, pulls in context from your existing backlog so nothing gets duplicated, and files them to the right epic ready for grooming.