Workflows describe how AI tools are combined to automate tasks and processes across real-world systems. Rather than focusing on individual tools, this section explains how different components work together to solve practical problems.
Content in this hub is educational rather than tool-centric. The emphasis is on patterns, logic, and decision points that apply across platforms, with optional references to specific tools where relevant.
This page links to workflow guides and real use cases, providing structure and context for readers who want to understand how AI workflows are designed, implemented, and maintained in practice.
What are agentic workflows? A practical explanation using Google Anti-Gravity
The term agentic workflows is being used frequently, but often without clear explanation. To understand what they are, it helps […]
Hosting Anti-Gravity agentic workflows in the cloud with Modal
Running agentic workflows locally works well for experimentation, but it introduces a reliability problem. If a computer shuts down or […]
Building your first agentic workflow with Google Anti-Gravity
Most automation platforms require users to understand every node, connector, and execution detail before anything works. Google’s Anti-Gravity takes a […]
The ultimate AI research workflow: combining Perplexity and NotebookLM
Research is no longer about collecting information. It is about selecting sources, validating claims, and turning insights into usable outputs. […]
n8n review: Extract text from any file with AI workflows
Many automation workflows fail at the same point: information is locked inside files. PDFs, scanned receipts, images, audio recordings, spreadsheets, […]