Workflow Automation
New AI Features and Multi-Model Engineering Workflows
The most productive AI users are not loyal to one model. These three articles cover Zapier’s new multi-model workflow support, one engineer’s system for matching different AI tools to different work modes, and how a six-person engineering team each built their own AI stack.
New AI and Workflow Features to Build Powerful Systems
TLDR: Zapier added multi-model support for GPT, Anthropic, and Gemini, plus the ability to convert Zaps into AI Agents and use global variables. The focus is on routing tasks to the right model at the right cost.
Key Insight: Route different tasks to different AI models — use the cheapest model that meets the quality bar.
How I 10x My Engineering with AI
TLDR: An engineer describes three distinct AI workflows: Windsurf and Cursor for daily coding, Claude for research and reasoning, and Claude Code for delegating entire tasks. 100% of pull requests are now AI-opened.
Key Insight: Match different AI tools to different modes of work.
Inside the AI Workflows of Every’s Six Engineers
TLDR: Six engineers at Every each customized their own AI stacks, ranging from Claude Code to Cursor to Codex. No two setups are alike, and the team’s productivity comes from individual experimentation rather than standardization.
Key Insight: Most productive teams let individuals build their own AI stacks.
What does this mean for your workflow?
The pattern across all three pieces is the same: the best results come from using multiple AI tools, each matched to a specific task or work mode. Do not standardize on one model. Experiment with routing different work to different tools, and let cost and quality — not brand loyalty — drive your choices.